Publications:

  1. Mourdjis, Philip J., Cowling, Peter I. and Robinson, Martin. 2014. Evolutionary Computation in Combinatorial Optimization. Evolutionary Computation in Combinatorial Optimization, Ed: Blum, C. and Ochoa, G., Springer-Verlag:170--181, Berlin Heidelberg,
    ID article: 3371


  2. Chen, Yujie, Cowling, Peter and Remde, Stephen. 2014. Evolutionary Computation in Combinatorial Optimisation. Evolutionary Computation in Combinatorial Optimisation, Ed: Blum, Christian and Ochoa, Gabriela, Springer Berlin Heidelberg, Lecture Notes in Computer Science, 8600:109-120,
    http://dx.doi.org/10.1007/978-3-662-44320-0_10 ,
    ID article: 3370


  3. Can, Burcu and Manandhar, Suresh. 2013. An Agglomerative Hierarchical Clustering Algorithm for Morpheme Labelling. Proceedings of the 9th International Conference on Recent Advances in Natural Language Processing, RANLP '13:129--135,
    http://lml.bas.bg/ranlp2013/docs/RANLP_main.pdf ,
    ID article: 3362


  4. Can, Burcu and Manandhar, Suresh. October 2013. Dirichlet Processes for Joint Learning of Morphology and PoS Tags. Proceedings of the Sixth International Joint Conference on Natural Language Processing, Asian Federation of Natural Language Processing:1087--1091, Nagoya, Japan,
    http://www.aclweb.org/anthology/I13-1152 ,
    ID article: 3363


  5. Suresh Manandhar and Deniz Yuret (Editors). June 2013. Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), Association for Computational Linguistics, Atlanta, Georgia, USA,
    http://www.aclweb.org/anthology/S13-2 ,
    ID article: 3369


  6. Klapaftis, Ioannis P. and Manandhar, Suresh. 2013. Evaluating Word Sense Induction and Disambiguation Methods. Language Resources and Evaluation, Springer Netherlands:1-27,
    http://dx.doi.org/10.1007/s10579-012-9205-0 ,
    ID article: 3361


  7. Sam Devlin and Daniel Kudenko. 2012. Dynamic Potential-Based Reward Shaping. Proceedings of The Eleventh Annual International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
    http://www.cs.york.ac.uk/aig/papers/devlin-kudenko-aamas2012.pdf ,
    ID article: 3349


  8. Suresh Manandhar and Deniz Yuret (Editors). 2012. Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), Association for Computational Linguistics, Montréal, Canada,
    http://www.aclweb.org/anthology/S12-1 ,
    ID article: 3368


  9. Burcu Can and Suresh Manandhar. 2012. Probabilistic Hierarchical Clustering Of Morphological Paradigms. 13th Conference of the European Chapter of the Association for computational Linguistics (EACL-2012), Errata : In Equation 6 - beta_s^(K-1) should be beta_s^K. In Equation 7: all alpha should be beta_m.:654 - 663,
    http://aclweb.org/anthology-new/E/E12/E12-1067.pdf ,
    ID article: 3356


  10. M. G. Ceddia, M. Bartlett, C. De Lucia and C. Perrings. 2011. On the regulation of spatial externalities: coexistence between GM and conventional crops in the EU and the newcomer principle. Australian Journal of Agricultural and Resource Economics, Wiley, 55(1):126-143,
    http://dx.doi.org/10.1111/j.1467-8489.2010.00518.x ,
    ID article: 3333.

    Abstract:
    Pollen-mediated gene flow is one of the main concerns associated with the introduction of genetically modified (GM) crops. Should a premium for non-GM varieties emerge on the market, ‘contamination’ by GM pollen would generate a revenue loss for growers of non-GM varieties. This paper analyses the problem of pollen-mediated gene flow as a particular type of production externality. The model, although simple, provides useful insights into coexistence policies. Following on from this and taking GM herbicide-tolerant oilseed rape (Brassica napus) as a model crop, a Monte Carlo simulation is used to generate data and then estimate the effect of several important policy variables (including width of buffer zones and spatial aggregation) on the magnitude of the externality associated with pollen-mediated gene flow.


  11. Ed: Frank Guerin, John Alexander, Philip Quinlan, Dimitar Kazakov and George Tsoulas. April 2011. Proc. of the Symposium on Computational Models of Cognitive Development, Ed: Frank Guerin, John Alexander, Philip Quinlan, Dimitar Kazakov and George Tsoulas, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3298


  12. M. Bartlett, I. Bate, J. Cussens and D. Kazakov. 2011. Probabilistic Instruction Cache Analysis using Bayesian Networks. Proceedings of the 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2011),
    http://www.cs.york.ac.uk/aig/papers/Bartlett_2011.pdf ,
    ID article: 3314.

    Abstract:
    Current approaches to instruction cache analysis for determining worst-case execution time rely on building a mathematical model of the cache that tracks its contents at all points in the program. This requires perfect knowledge of the functional behaviour of the cache and may result in extreme complexity and pessimism if many alternative paths through code sections are possible. To overcome these issues, this paper proposes a new hybrid approach in which information obtained from program traces is used to automate the construction of a model of how the cache is used. The resulting model involves the learning of a Bayesian network that predicts which instructions result in cache misses as a function of previously taken paths. The model can then be utilised to predict cache misses for previously unseen inputs and paths. The accuracy of this learned model is assessed against real benchmarks and an established statistical approach to illustrate its benefits.


  13. Yuan, J., Yao, L., Yuan, T., Hao, Z. and Liu, F.. 2011. Multi-party Dialogue Games for Distributed Argumentation System, In Proceedings of 2011 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, Lyon,
    Yorkcategory: D - refereed international conference paper,
    ID article: 3223


  14. A. Alzaidi and D. Kazakov. 2011. Equation Discovery for Financial Forecasting in the Context of Islamic Banking. Proc. of the Eleventh IASTED Intl Conf. on Artificial Intelligence and Applications (AIA 2011), Innsbruck, Austria,
    ID article: 3306


  15. Ed: Dimitar Kazakov and Preslav Nakov and Ahmad R. Shahid and George Tsoulas. April 2011. Proc. of the Symposium on Learning Language Models from Multilingual Corpora, Ed: Dimitar Kazakov and Preslav Nakov and Ahmad R. Shahid and George Tsoulas, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3301


  16. Yuan, T. and Kelly, T.. 2011. Argument-based Approach to Computer System Safety Engineering. In Press for International Journal of Critical Computer-based Systems,
    Yorkcategory: C - refereed journal paper,
    ID article: 3216


  17. Li, Shuguang and Manandhar, Suresh. June 2011. Improving Question Recommendation by Exploiting Information Need. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics:1425--1434, Portland, Oregon, USA,
    http://www.aclweb.org/anthology/P11-1143 ,
    ID article: 3246


  18. Reddy, Siva, McCarthy, Diana and Manandhar, Suresh. November 2011. An Empirical Study on Compositionality in Compound Nouns. Proceedings of 5th International Joint Conference on Natural Language Processing (IJCNLP-2011), Asian Federation of Natural Language Processing:210--218, Chiang Mai, Thailand,
    http://www.aclweb.org/anthology/I/I11/I11-1024.pdf ,
    ID article: 3268.

    Abstract:
    A multiword is compositional if its meaning can be expressed in terms of the meaning of its constituents. In this paper, we collect and analyse the compositionality judgments for a range of compound nouns using Mechanical Turk. Unlike existing compositionality datasets, our dataset has judgments on the contribution of constituent words as well as judgments for the phrase as a whole. We use this dataset to study the relation between the judgments at constituent level to that for the whole phrase. We then evaluate two different types of distributional models for compositionality detection - constituent based models and composition function based models. Both the models show competitive performance though the composition function based models perform slightly better. In both types, additive models perform better than their multiplicative counterparts.
    Dataset: http://www.cs.york.ac.uk/aig/nl/datasets/compositionalityDataset/ijcnlp_compositionality_data.tgz
    Presentation: http://www.cs.york.ac.uk/aig/nl/datasets/compositionalityDataset/EmpStdyComp.pdf


  19. Yuan, T., Moore, D., Reed, C. and Ravenscroft, A.. 2011. Informal Logic Dialogue Games in Human-Computer Dialogue. Knowledge Engineering Review, 26(2):159-174,
    Yorkcategory: C - refereed journal paper,
    ID article: 3220


  20. Ed: Simon O'Keefe and Dimitar Kazakov and George Tsoulas. April 2011. Proc. of the Symposium on Active Vision, Ed: Simon O'Keefe and Dimitar Kazakov and George Tsoulas, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3296


  21. Ed: Aladdin Ayesh and Mark Bishop and John Barnden and Dimitar Kazakov and George Tsoulas. April 2011. Proc. of the Symposium on Towards a Comprehensive Intelligence Test, Ed: Aladdin Ayesh and Mark Bishop and John Barnden and Dimitar Kazakov and George Tsoulas, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3304


  22. Ahmad R. Shahid and Dimitar Kazakov. April 2011. Using Multilingual Corpora to extract Semantic Information, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3238


  23. Klapaftis, Ioannis P., Pandey, Suraj and Manandhar, Suresh. 2011. Graph-Based Relation Mining. Multimedia Communications, Services and Security, Ed: Dziech, Andrzej and Czyzewski, Andrzej, Springer Berlin Heidelberg, 10.1007/978-3-642-21512-4_12, Communications in Computer and Information Science, 149:100-112, Krakow, Poland, [Best Paper Award],
    http://dx.doi.org/10.1007/978-3-642-21512-4_12 ,
    ID article: 3275.

    Abstract:
    Relationship mining or Relation Extraction (RE) is the task of identifying the different relations that might exist between two or more named entities. Relation extraction can be exploited in order to enhance the usability of a variety of applications, including web search, information retrieval, question answering and others. This paper presents a novel unsupervised method for relation extraction which casts the problem of RE into a graph-based framework. In this framework, entities are represented as vertices in a graph, while edges between vertices are drawn according to the distributional similarity of the corresponding entities. The RE problem is then formulated in a bootstrapping manner as an edge prediction problem, where in each iteration the target is to identify pairs of disconnected vertices (entities) most likely to share a relation.
    Presentation: http://www.cs.york.ac.uk/aig/nl/datasets/relationextractionDataset/mcss.pdf


  24. Siva Reddy and Serge Sharoff. November 2011. Cross Language POS Taggers (and other Tools) for Indian Languages: An Experiment with Kannada using Telugu Resources. Proceedings of IJCNLP workshop on Cross Lingual Information Access: Computational Linguistics and the Information Need of Multilingual Societies. (CLIA 2011 at IJNCLP 2011), Chiang Mai, Thailand,
    http://www.aclweb.org/anthology/W11-3603.pdf ,
    ID article: 3271


  25. Ozgur Akgun, Ian Miguel, Chris Jefferson and Brahim Hnich. 2011. Extensible Automated Constraint Programming. Proc. of the Twenty-Fifth AAAI Conf. on Artificial Intelligence:4-11,
    ID article: 3295


  26. Ed: Mark Bishop and Kevin Magill and Steve Russ and Yasemin J. Erden and Dimitar Kazakov and George Tsoulas. April 2011. Proc. of the Symposium on Computing and Philosophy, Ed: Mark Bishop and Kevin Magill and Steve Russ and Yasemin J. Erden and Dimitar Kazakov and George Tsoulas, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3299


  27. Sitsofe Wheeler, Iain Bate and Mark Bartlett. 2011. Video Subset Selection for Measurement Based Worst Case Execution Time Analysis. Proceedings of the 6th IEEE International Symposium on Industrial Embedded Systems (SIES'11),
    http://www.cs.york.ac.uk/aig/papers/Wheeler_2011.pdf ,
    ID article: 3315.

    Abstract:
    Worst Case Execution Time (WCET) has traditionally approached problems with small, well defined input spaces. For processes with a large input space (such as video) existing techniques struggle to producea meaningful result. This work investigates a technique that reducesthe input space while still preserving execution time properties to allow subsequent WCET analysis to be more effective.


  28. Sam Devlin, Marek Grzes and Daniel Kudenko. 2011. An Empirical Study of Potential-Based Reward Shaping and Advice in Complex, Multi-Agent Systems. Advances in Complex Systems, 14(2):251-278,
    http://www.cs.york.ac.uk/aig/papers/devlin-grzes-kudenko-acs2011.pdf ,
    ID article: 3346


  29. Ed: Ron Chrisley and Rob Clowes and Steve Torrance and Dimitar Kazakov and George Tsoulas. April 2011. Proc. of the Symposium on Machine Consciousness, Ed: Ron Chrisley and Rob Clowes and Steve Torrance and Dimitar Kazakov and George Tsoulas, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3302


  30. Reddy, Siva, Klapaftis, Ioannis, McCarthy, Diana and Manandhar, Suresh. November 2011. Dynamic and Static Prototype Vectors for Semantic Composition. Proceedings of 5th International Joint Conference on Natural Language Processing (IJCNLP-2011), Asian Federation of Natural Language Processing:705--713, Chiang Mai, Thailand,[Best Paper Award],
    http://www.aclweb.org/anthology/I/I11/I11-1079.pdf ,
    ID article: 3273.

    Abstract:
    Compositional Distributional Semantic methods model the distributional behavior of a compound word by exploiting the distributional behavior of its constituent words. In this setting, a constituent word is typically represented by a feature vector conflating all the senses of that word. However, not all the senses of a constituent word are relevant when composing the semantics of the compound. In this paper, we present two different methods for selecting the relevant senses of constituent words. The first one is based on Word Sense Induction and creates a static multi prototype vectors representing the senses of a constituent word. The second creates a single dynamic prototype vector for each constituent word based on the distributional properties of the other constituents in the compound. We use these prototype vectors for composing the semantics of noun-noun compounds and evaluate on a compositionality-based similarity task. Our results show that: (1) selecting relevant senses of the constituent words leads to a better semantic composition of the compound, and (2) dynamic prototypes perform better than static prototypes.
    Presentation:http://www.cs.york.ac.uk/aig/nl/datasets/compositionalityDataset/DynPrp.pdf


  31. Sam Devlin and Daniel Kudenko. 2011. Theoretical Considerations of Potential-Based Reward Shaping for Multi-Agent Systems. Proceedings of The Tenth Annual International Conference on Autonomous Agents and Multiagent Systems (AAMAS):225-232,
    http://www.cs.york.ac.uk/aig/papers/devlin-kudenko-aamas2011-theory.pdf ,
    ID article: 3351


  32. Ed: Daniela M. Romano and David C. Moffat and Dimitar Kazakov and George Tsoulas. April 2011. Proc. of the Symposium on AI and Games, Ed: Daniela M. Romano and David C. Moffat and Dimitar Kazakov and George Tsoulas, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3297


  33. M. Butler and D. Kazakov. 2011. The Effects of Variable Stationarity in a Financial Time-Series on Artificial Neural Networks. Proc. of the IEEE Symposium on Computational Intelligence for Financial Engineering and Economics CIFEr 2011, Paris, France,
    ID article: 3305


  34. Ed: Wan Ching Ho and Mei Yii Lim and Cyril Brom and Dimitar Kazakov and George Tsoulas. April 2011. Proc. of the Symposium on Human Memory for Artificial Agents, Ed: Wan Ching Ho and Mei Yii Lim and Cyril Brom and Dimitar Kazakov and George Tsoulas, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3300


  35. B. Ziólko, S. Manandhar, R. C. Wilson and M. Ziólko. 2011. Phoneme Segmentation Based on Wavelet Spectra Analysis. Archives of Acoustics, 36(1):29-47,
    ID article: 3357


  36. Reddy, Siva, McCarthy, Diana, Manandhar, Suresh and Gella, Spandana. June 2011. Exemplar-Based Word-Space Model for Compositionality Detection: Shared Task System Description. Proceedings of the Workshop on Distributional Semantics and Compositionality, Association for Computational Linguistics:54--60, Portland, Oregon, USA,
    ttp://www.aclweb.org/anthology/W11-1310 ,
    ID article: 3245


  37. Sam Devlin, Marek Grzes and Daniel Kudenko. 2011. Multi-Agent, Reward Shaping for RoboCup KeepAway (Extended Abstract). Proceedings of The Tenth Annual International Conference on Autonomous Agents and Multiagent Systems (AAMAS):1227-1228,
    http://www.cs.york.ac.uk/aig/papers/devlin-grzes-kudenko-aamas2011-empirical.pdf ,
    ID article: 3350


  38. Ed: Alan M. Frisch and Barry O'Sullivan. April 2011. Proceedings of the ERCIM Workshop on Constraint Solving and Constraint Logic Programming, Ed: Alan M. Frisch and Barry O'Sullivan,
    http://csclp2011.cs.st-andrews.ac.uk/csclp2011proceedings.pdf ,
    ID article: 3294


  39. Ed: Matthias Mailliard and Clara Smith and Frédéric Amblard and Samuel Thiriot and Dimitar Kazakov and George Tsoulas. April 2011. Proc. of the Symposium on Social Networks and Multiagent Systems, Ed: Matthias Mailliard and Clara Smith and Frédéric Amblard and Samuel Thiriot and Dimitar Kazakov and George Tsoulas, The UK Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB’11 Convention, York, United Kingdom,
    ID article: 3303


  40. Yuan, T. and Kelly, T.. 2011. Argument Schemes in Computer System Safety Engineering.. In Press for Informal Logic,
    Yorkcategory: C - refereed journal paper,
    ID article: 3219


  41. Klapaftis, Ioannis P. and Manandhar, Suresh. June 2010. Taxonomy Learning Using Word Sense Induction. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics(NAACL-HLT), Association for Computational Linguistics:82--90, Los Angeles, California,
    http://www.aclweb.org/anthology/N10-1010 ,
    ID article: 3189


  42. Pierre Andrews and Suresh Manandhar. January 2010. A SVM Cascade for Agreement/Disagreement Classification.. The TAL Journal, Special Issue in Machine Learning, Volume 50(3):89-107,
    www.atala.org/IMG/pdf/TAL-2009-3-03-Andrews.pdf ,
    ID article: 3180


  43. Yuan, T. and Xu, T.. 2010. Computer System Safety Argument Schemes, In Proceedings of the Second World congress on Software Engineering (WCSE 2010), 2:107-110, Wuhan, China,
    Yorkcategory: D - refereed international conference paper,
    ID article: 3224


  44. Matthew Butler and Vlado Keselj. 2010. Data Mining Techniques for Proactive Fault Diagnostics of Electronic Gaming Machines. Proceedings of Canadian AI'2010, Ottawa, ON, Canada,
    ID article: 3108


  45. Alan M. Frisch and Paul A. Giannaros. September 2010. SAT Encodings of the AT-Most- extitk Constraint: Some Old, Some New, Some Fast, Some Slow. Proc. of the 9th Int. Workshop on Constraint Modelling and Reformulation,
    ID article: 3293


  46. Ismail, Azniah and Manandhar, Suresh. August 2010. Bilingual lexicon extraction from comparable corpora using in-domain terms. COLING 2010: Posters, Coling 2010 Organizing Committee:481--489, Beijing, China,
    http://www.aclweb.org/anthology/C10-2055 ,
    ID article: 3186


  47. Korkontzelos, Ioannis and Manandhar, Suresh. June 2010. Can Recognising Multiword Expressions Improve Shallow Parsing?. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics(NAACL-HLT), Association for Computational Linguistics:636--644, Los Angeles, California,
    http://www.aclweb.org/anthology/N10-1089 ,
    ID article: 3190


  48. Zanzotto, Fabio Massimo, Korkontzelos, Ioannis, Fallucchi, Francesca and Manandhar, Suresh. August 2010. Estimating Linear Models for Compositional Distributional Semantics. Proceedings of the 23rd International Conference on Computational Linguistics (COLING ), Coling 2010 Organizing Committee:1263--1271, Beijing, China,
    http://www.aclweb.org/anthology/C10-1142 ,
    ID article: 3185


  49. M. Bartlett, I. Bate and J. Cussens. 2010. Learning Bayesian Networks for Improved Instruction Cache Analysis. Proceedings of the 9th International Conference on Machine Learning and Applications:417-423,
    http://www.cs.york.ac.uk/aig/papers/Bartlett_2010b.pdf ,
    ID article: 3318.

    Abstract:
    As modern processors can execute instructions at far greater rates than these instructions can be retrieved from main memory, computer systems commonly include caches that speed up access times. While these improve average execution times, they introduce additional complexity in determining the Worst Case Execution Times crucial for Real-Time Systems. In this paper, an approach is presented that utilises Bayesian Networks in order to more accurately estimate the worst-case caching behaviour of programs. With this method, a Bayesian Network is learned from traces of program execution that allows both constructive and destructive dependencies between instructions to be determined and a joint distribution over the number of cache hits to be found. Attention is given to the question of how the accuracy of the network depends on both the number of observations used for learning and the cardinality of the set of potential parents considered by the learning algorithm.


  50. Manandhar, Suresh, Klapaftis, Ioannis, Dligach, Dmitriy and Pradhan, Sameer. July 2010. SemEval-2010 Task 14: Word Sense Induction & Disambiguation. Proceedings of the 5th International Workshop on Semantic Evaluation(SemEval), Association for Computational Linguistics:63--68, Uppsala, Sweden,
    http://www.aclweb.org/anthology/S10-1011 ,
    ID article: 3188


  51. Matthew Butler and Dimitar Kazakov. July 2010. Modeling the Behaviour of the Stock Market with an Artificial Immune System. Proceedings of IEEE CEC, Barcelona, Spain,
    ID article: 3109


  52. M. Bartlett, I. Bate and D. Kazakov. 2010. Accurate Determination of Loop Iterations for Worst-Case Execution Time Analysis. IEEE Transactions on Computers, 59(11):1520 - 1532,
    http://dx.doi.org/10.1109/TC.2010.59 ,
    ID article: 3316.

    Abstract:
    Determination of accurate estimates for the Worst-Case Execution Time of a program is essential for guaranteeing the correct temporal behavior of any Real-Time System. Of particular importance is tightly bounding the number of iterations of loops in the program or excessive undue pessimism can result. This paper presents a novel approach to determining the number of iterations of a loop for such analysis. Program traces are collected and analyzed allowing the number of loop executions to be parametrically determined safely and precisely under certain conditions. The approach is mathematically proved to be safe and its practicality is demonstrated on a series of benchmarks.


  53. Klapaftis, Ioannis and Manandhar, Suresh. October 2010. Word Sense Induction & Disambiguation Using Hierarchical Random Graphs. Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP, Association for Computational Linguistics:745--755, Cambridge, MA,
    http://www.aclweb.org/anthology/D10-1073 ,
    ID article: 3184


  54. Ioannis P. Klapaftis, Suresh Manandhar. 2010. Unsupervised Named Entity Resolution. Proceedings of the 3rd IEEE International Conference on Multimedia Communications, Services and Security, IEEE Computer Society, Krakow, Poland,
    http://www-users.cs.york.ac.uk/~suresh/papers/UNER.pdf ,
    ID article: 3119


  55. Yuan, T., Moore, D. and Grierson, A.. 2010. Assessing Debate Strategies via Computational Agents. Argument and Computation, rss, 1(3):215-248,
    Yorkcategory: C - refereed journal paper,
    ID article: 3221


  56. Yuan, T., Moore, D., Ravenscroft, A. and Zhong, G.. 2010. Evaluation of a Human-Computer Dialogue System for Educational Debate., In Proceedings of the Second Global Congress on Intelligent Systems (GCIS 2010), 2:359-362, Wuhan, China,
    Yorkcategory: D - refereed international conference paper,
    ID article: 3222


  57. Z. M. Hira and M. Bartlett. 2010. Simulating creole and dialect formation. The Evolution of Language: Proceedings of the 8th International Conference:419-420,
    http://www.cs.york.ac.uk/aig/papers/Hira_2010.pdf ,
    ID article: 3334


  58. Korkontzelos, Ioannis and Manandhar, Suresh. July 2010. UoY: Graphs of Unambiguous Vertices for Word Sense Induction and Disambiguation. Proceedings of the 5th International Workshop on Semantic Evaluation(SEMEVAL), Association for Computational Linguistics(ACL):355--358, Uppsala, Sweden,
    http://www.aclweb.org/anthology/S10-1079 ,
    ID article: 3187


  59. M. Bartlett, I. Bate and J. Cussens. 2010. Instruction Cache Prediction Using Bayesian Networks. Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010):1099-1100, Lisbon, Portugal,
    http://www.cs.york.ac.uk/aig/papers/Bartlett_2010.pdf ,
    ID article: 3317.

    Abstract:
    Storing instructions in caches has led to dramatic increases in the speed at which programs can execute. However, this has also made it harder to reason about the time needed for execution in those domains where temporal behaviour of code is important. This paper presents a novel approach to predicting which instructions will be found in the cache when required using machine learning. More specifically, we demonstrate a method in which a Bayesian network is inferred from examples of a program running and is then used to predict the presence of instructions in the cache when the same program is run with unknown inputs.


  60. Matthew Butler and Dimitar Kazakov. September 2010. Optimizing Bollinger Bands via Particle Swarm Optimization. Proceedings of ANTS - 7th Int'l Conference on Swarm Intelligence, Brussels, Belgium,
    ID article: 3113


  61. Rania Hodhod, Daniel Kudenko and Paul Cairns. April 2009. Serious Games to Teach Ethics. In proceedings of AISB'09: Artificial and Ambient Intelligence, Edinburgh, Scotland, UK,
    ID article: 3047


  62. Dimitar Kazakov and Tsvetomira Tsenova. January 2009. Equation Discovery for Macroeconomic Modelling. International Conference on Agents and Artificial Intelligence, Porto, Portugal,
    ID article: 3072


  63. Sam Devlin, Marek Grzes and Daniel Kudenko. 2009. Reinforcement Learning in RoboCup KeepAway with Partial Observability. Proceedings of the 2009 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT:201-208,
    http://www.cs.york.ac.uk/aig/papers/devlin-grzes-kudenko-iat2009.pdf ,
    ID article: 3345


  64. Ed: Suresh Manandhar and Ioannis P. Klapaftis. June 2009. Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics, Ed: Suresh Manandhar and Ioannis P. Klapaftis, Association for Computational Linguistics, Boulder, Colorado, USA,
    http://www.aclweb.org/anthology/W09-17 ,
    ID article: 3114


  65. Hodhod R., Kudenko D. and Cairns P. July 2009. Educational Narrative and Student Modeling for Ill-Defined Domains. In proceedings of AIED 2009: Artificial Intelligence for Education, Brighton, UK,
    ID article: 2984


  66. M. G. Ceddia, M. Bartlett and C. Perrings. 2009. Quantifying the effect of buffer zones, crop areas and spatial aggregation on the externalities of genetically modified crops at landscape level. Agriculture, Ecosystems & Environment, Elsevier, 129(1):65-72,
    http://dx.doi.org/10.1016/j.agee.2008.07.004 ,
    ID article: 3322.

    Abstract:
    The development of genetically modified (GM) crops has led the European Union (EU) to put forward the concept of ‘coexistence’ to give farmers the freedom to plant both conventional and GM varieties. Should a premium for non-GM varieties emerge in the market, ‘contamination’ by GM pollen would generate a negative externality to conventional growers. It is therefore important to assess the effect of different ‘policy variables’ on the magnitude of the externality to identify suitable policies to manage coexistence. In this paper, taking GM herbicide tolerant oilseed rape as a model crop, we start from the model developed in Ceddia et al. [Ceddia, M.G., Bartlett, M., Perrings, C., 2007. Landscape gene flow, coexistence and threshold effect: the case of genetically modified herbicide tolerant oilseed rape (Brassica napus). Ecol. Modell. 205, pp. 169–180] use a Monte Carlo experiment to generate data and then estimate the effect of the number of GM and conventional fields, width of buffer areas and the degree of spatial aggregation (i.e. the ‘policy variables’) on the magnitude of the externality at the landscape level. To represent realistic conditions in agricultural production, we assume that detection of GM material in conventional produce might occur at the field level (no grain mixing occurs) or at the silos level (where grain mixing from different fields in the landscape occurs). In the former case, the magnitude of the externality will depend on the number of conventional fields with average transgenic presence above a certain threshold. In the latter case, the magnitude of the externality will depend on whether the average transgenic presence across all conventional fields exceeds the threshold. In order to quantify the effect of the relevant ‘policy variables’, we compute the marginal effects and the elasticities. Our results show that when relying on marginal effects to assess the impact of the different ‘policy variables’, spatial aggregation is far more imp


  67. Marek Grzes and Daniel Kudenko. 2009. Learning Shaping Rewards in Model-based Reinforcement Learning. Proceedings of the AAMAS'09 Workshop on Adaptive and Learning Agents (ALA'09):9--16,
    Yorkcategory: D,
    ID article: 3068


  68. Burcu Can and Suresh Manandhar. 2009. Unsupervised Learning of Morphology by Using Syntactic Categories. Working Notes for the CLEF 2009 Workshop, Ed: Francesca Borri and Alessandro Nardi and Carol Peters, Corfu, Greece,
    http://clef-campaign.org/2009/working_notes/morpho-papers/can-paperCLEF2009.pdf ,
    ID article: 3104


  69. Rania Hodhod. July 2009. Educational Narrative-Based Environment to Teach Ethics. In proceedings of young researchers track (YRT). Held at the 14th International Conference on Artificial Intelligence in Education (AIED09), Brighton, UK,
    ID article: 3048


  70. Korkontzelos, Ioannis and Manandhar, Suresh. August 2009. Detecting Compositionality in Multi-Word Expressions.. Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, Association for Computational Linguistics:65--68, Suntec, Singapore,
    http://www.aclweb.org/anthology/P/P09/P09-2017 ,
    ID article: 3135


  71. M. Arinbjarnar and D. Kudenko. 2009. Directed Emergent Drama vs. Pen & Paper Role-Playing Games. AISB'09 Symposium: AI & Games, Edinburgh, UK,
    ID article: 3010


  72. Malik Tahir Hassan, Asim Karim, Suresh Manandhar and James Cussens. September 2009. ECML PKDD Discovery Challenge 2009 (DC09). ECML PKDD Discovery Challenge 2009 (DC09), Ed: Folke Eisterlehner and Andreas Hotho and Robert Jäschke, CEUR Workshop Proceedings, 497:85--97, Bled, Slovenia,
    http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-497/ ,
    ID article: 3133


  73. Naim, R., Moore, D. and Yuan, T.. 2009. Computational Dialectics for Computer Based Learning, In Proceedings of the 2009 International conference on the Current Trends in Information Technology (CTIT’09), Dubai,
    Yorkcategory: D - refereed international conference paper,
    ID article: 3225


  74. Dimitar Kazakov. 2009. Simulating the Benefits of Language. Conf. on Ways to Protolanguage: the initial stages of the evolution of the language faculty, Torun, Poland,
    ID article: 3077


  75. Marek Grzes and Daniel Kudenko. 2009. Reinforcement Learning with Reward Shaping and Mixed Resolution Function Approximation. International Journal of Agent Technologies and Systems (IJATS), 1(2):36-54,
    Yorkcategory: C,
    ID article: 3066


  76. Andrews, Pierre and Manandhar, Suresh. April 2009. Measure Of Belief Change as an Evaluation of Persuasion. Proceedings of the AISB'09 Persuasive Technology and Digital Behaviour Intervention Symposium, Ed: Masthoff, Judith and Grasso, Floriana,
    http://www.aisb.org.uk/convention/aisb09/Proceedings/PERSUASIVE/FILES/AndrewsP.pdf ,
    ID article: 3173


  77. M. Bartlett, I. Bate and D. Kazakov. 2009. Guaranteed Loop Bound Identification from Program Traces for WCET. Proceedings of the 15th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), San Francisco, CA, United States,
    http://www.cs.york.ac.uk/aig/papers/Bartlett_2009.pdf ,
    ID article: 3319.

    Abstract:
    Static analysis can be used to determine safe estimates of Worst Case Execution Time. However, overestimation of the number of loop iterations, particularly in nested loops, can result in substantial pessimism in the overall estimate. This paper presents a method of determining exact parametric values of the number of loop iterations for a particular class of arbitrarily deeply nested loops. It is proven that values are guaranteed to be correct using information obtainable from a finite and quantifiable number of program traces. Using the results of this proof, a tool is constructed and its scalability assessed.


  78. Ioannis Korkontzelos, Ioannis Klapaftis and Suresh Manandhar. June 2009. Graph Connectivity Measures for Unsupervised Parameter Tuning of Graph-Based Sense Induction Systems.. Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics, Association for Computational Linguistics:36--44, Boulder, Colorado, USA,
    http://www.aclweb.org/anthology/W/W09/W09-1705 ,
    ID article: 3090.

    Abstract:
    Word Sense Induction (WSI) is the task of identifying the different senses (uses) of a target word in a given text. This paper focuses on the unsupervised estimation of the free parameters of a graph-based WSI method, and explores the use of eight Graph Connectivity Measures (GCM) that assess the degree of connectivity in a graph. Given a target word and a set of parameters, GCM evaluate the connectivity of the produced clusters, which correspond to subgraphs of the initial (unclustered) graph. Each parameter setting is assigned a score according to one of the GCM and the highest scoring setting is then selected. Our evaluation on the nouns of SemEval-2007 WSI task (SWSI) shows that: (1) all GCM estimate a set of parameters which significantly outperform the worst performing parameter setting in both SWSI evaluation schemes, (2) all GCM estimate a set of parameters which outperform the Most Frequent Sense (MFS) baseline by a statistically significant amount in the supervised evaluation scheme, and (3) two of the measures estimate a set of parameters that performs closely to a set of parameters estimated in supervised manner.


  79. Silvia Quarteroni and Suresh Manandhar. January 2009. Designing an Interactive Open-domain Question Answering System. Natural Language Engineering, volume 15, issue 01, pp. 73-95,
    http://journals.cambridge.org/repo_A28X8NbZ ,
    ID article: 2954


  80. Alan M. Frisch and Peter J. Stuckey. September 2009. The Proper Treatment of Undefinedness in Constraint Programming. Principles and Practice of Constraint Programming --- CP 2009, Ed: Ian Gent, Springer-Verlag, LNAI, 5732:367-382,
    ID article: 3292


  81. M. Arinbjarnar, H. Barber and D. Kudenko. April 2009. A Critical Review of Interactive Drama Systems. AISB'09 Symposium: AI & Games, Edinburgh, UK,
    ID article: 3011


  82. Rania Hodhod, Daniel Kudenko and Paul Cairns. July 2009. AEINS: Adaptive Educational Interactive Narrative System to Teach Ethics. In proceedings of workshop on intelligent educational games. Held at the 14th International Conference on Artificial Intelligence in Education (AIED09), Brighton, UK,
    ID article: 3049


  83. Li, Shuguang and Manandhar, Suresh. August 2009. Automatic Generation of Information-seeking Questions Using Concept Clusters. Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, Association for Computational Linguistics:93--96, Suntec, Singapore,
    http://www.aclweb.org/anthology/P/P09/P09-2024 ,
    ID article: 3134


  84. Dimitar Kazakov and George Tsoulas. 2009. Applying Recapitulation Theory to Language. Conf. on Ways to Protolanguage: the initial stages of the evolution of the language faculty, Torun, Poland,
    ID article: 3078


  85. Marek Grzes and Daniel Kudenko. 2009. Improving Optimistic Exploration in Model-free Reinforcement Learning. Proceedings of the 9th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA'09), Springer-Verlag, LNCS,
    Yorkcategory: D,
    ID article: 3067


  86. Manandhar, Suresh and Klapaftis, Ioannis. June 2009. SemEval-2010 Task 14: Evaluation Setting for Word Sense Induction & Disambiguation Systems. Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions (SEW-2009), Association for Computational Linguistics:117--122, Boulder, Colorado,
    http://www.aclweb.org/anthology/W09-2419 ,
    ID article: 3136


  87. Azniah Ismail and Suresh Manandhar. June 2009. Utilizing Contextually Relevant Terms in Bilingual Lexicon Extraction. Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics, Association for Computational Linguistics:10-17, Boulder, Colorado, USA,
    http://aclweb.org/anthology-new/W/W09/W09-1702.pdf ,
    ID article: 3098


  88. Ahmad R. Shahid and Dimitar Kazakov. January 2009. Automatic Multilingual Lexicon Generation using Wikipedia as a Resource. International Conference on Agents and Artificial Intelligence, Porto, Portugal,
    http://www-users.cs.york.ac.uk/~ahmad/index.html ,
    ID article: 2960


  89. Bartosz Zi olko, Suresh Manandhar, Richard C. Wilson and Mariusz Zi olko. 2008. Application of HTK to the Polish Language. Proceedings of IEEE International Conference on Audio, Language and Image Processing, Shanghai,
    http://www-users.cs.york.ac.uk/~suresh/papers/HTKPOLISH.pdf ,
    ID article: 3199


  90. M. Bartlett, I. Bate and D. Kazakov. 2008. Challenges in Relational Learning for Real Time Systems Applications. Proceedings of the 18th International Conference on Inductive Logic Programming, ILP 2008, Ed: F. Železný and N. Lavrac, Springer, Lecture Notes in Computer Science, 5194:42-58, Prague, Czech Republic,
    http://www.cs.york.ac.uk/aig/papers/Bartlett_2008.pdf ,
    ID article: 3320.

    Abstract:
    The problem of determining the Worse Case Execution Time (WCET) of a piece of code is a fundamental one in the Real Time Systems community. Existing methods either try to gain this information by analysis of the program code or by running extensive timing analyses. This paper presents a new approach to the problem based on using Machine Learning in the form of ILP to infer program properties based on sample executions of the code. Additionally, significant improvements in the range of functions learnable and the time taken for learning can be made by the application of more advanced ILP techniques.


  91. B. Ziólko, S. Manandhar, R. C. Wilson and M. Ziólko. 2008. LogitBoost Weka Classifier Speech Segmentation. Proceedings of 2008 IEEE International Conference on Multimedia & Expo, Hannover,
    ID article: 2955.

    Abstract:
    Segmenting the speech signals on the basis of time-frequency analysis is the most natural approach. Boundaries are located in places where energy of some frequency subband rapidly changes. Speech segmentation method which bases on discrete wavelet transform, the resulting power spectrum and its derivatives is presented. This information allows to locate the boundaries of phonemes. A statistical classification method was used to check which features are useful. The efficiency of segmentation was verified on a male speaker taken from a corpus of Polish language.


  92. Hodhod R. and Kudenko D. June 2008. Interactive Narrative and Intelligent Tutoring for Ill Defined Domains. In proceedings of a workshop held during ITS-2008: ITSs for Ill-Structured Domains Focusing on Assessment and Feedback. The 9th international Conference on Intelligent Tutoring Systems, Montreal, Canada,
    ID article: 2981


  93. Marek Grzes and Daniel Kudenko. 2008. An Empirical Analysis of the Impact of Prioritised Sweeping on the DynaQ's Performance. Proceedings of the 9th International Conference on Artificial Intelligence and Soft Computing (ICAISC'08), Springer-Verlag, LNAI:1041-1051,
    Yorkcategory: D,
    ID article: 3059


  94. Iain Bate and Dimitar Kazakov. June 2008. New Directions in Worst-Case Execution Time Analysis. IEEE Congress on Computational Intelligence (WCCI 2008), Hong Kong,
    ID article: 3073


  95. M. Arinbjarnar and D. Kudenko. December 2008. Schemas in Directed Emergent Drama. proceedings of the 1^st Joint International Conference on Interactive Digital Storytelling ICIDS08, Erfurt, Germany,
    ID article: 3009


  96. Heather Barber and Daniel Kudenko. January 2008. Generation of Dilemma-based Interactive Narratives with a Changeable Story Goal. In Proceedings of the International Conference on Intelligent Technologies for interactive entertainment, Gives an introduction to the dinosaur domain in the GADIN system, Playa del Carmen, Mexico,
    http://www-users.cs.york.ac.uk/~hmbarber/GADINwithstorygoal.pdf ,
    ID article: 2722


  97. Arturo L. Servin and Daniel Kudenko. 2008. Multi-agent Reinforcement Learning for Intrusion Detection. Lecture Notes in Computer Science, Springer, 4865:211-223,
    ID article: 2694


  98. Marek Grzes and Daniel Kudenko. 2008. Plan-based Reward Shaping for Reinforcement Learning. Proceedings of the 4th IEEE International Conference on Intelligent Systems (IS'08), IEEE:22-29,
    Yorkcategory: D,
    ID article: 3062


  99. Enda Ridge and Daniel Kudenko. 2008. Determining whether a problem characteristic affects heuristic performance. A rigorous Design of Experiments approach. Recent Advances in Evolutionary Computation for Combinatorial Optimization, Springer, Studies in Computational Intelligence,
    ID article: 2709.

    Abstract:
    This chapter presents a rigorous Design of Experiments (DOE) approach for determining whether a problem characteristic affects the performance of a heuristic. Specifically, it reports a study on the effect of the cost matrix standard deviation of symmetric Travelling Salesman Problem (TSP) instances on the performance of Ant Colony Optimisation (ACO) heuristics. Results demonstrate that for a given instance size, an increase in the standard deviation of the cost matrix of instances results in an increase in the difficulty of the instances. This implies that for ACO, it is insufficient to report results on problems classified only by problem size, as has been commonly done in most ACO research to date. Some description of the cost matrix distribution is also required when attempting to explain and predict the performance of these heuristics on the TSP. The study should serve as a template for similar investigations with other problems and other heuristics.


  100. Alan M. Frisch, Warwick Harvey Christopher Jefferson, Bernadette Martínez~Hernández and Ian Miguel. 2008. Essence: A Constraint Language for Specifying Combinaotrial Problems. Constraints,
    ID article: 2673


  101. Santos Costa, Vítor, Page, David and Cussens, James. 2008. CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. Probabilistic Inductive Logic Programming, Ed: De Raedt, Luc and Paolo Frasconi and Kristian Kersting and Stephen Muggleton, Springer, Lectures Notes in Computer Science, 4911:156--188, Berlin,
    http://dx.doi.org/10.1007/978-3-540-78652-8_6 ,
    ID article: 2687.

    Abstract:
    In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP( BN ) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP( BN ) programs. An implementation of CLP( BN ) is publicly available as part of YAP Prolog at http://www.ncc.up.pt/~vsc/Yap.


  102. Marek Grzes and Daniel Kudenko. 2008. Plan-based Reward Shaping for Reinforcement Learning. Proceedings of the AAMAS'08 Workshop on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS-ALAg'08):9--16,
    Yorkcategory: D,
    ID article: 3057


  103. James Cussens. 2008. Bayesian network learning by compiling to weighted MAX-SAT. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI 2008), AUAI Press:105--112, Helsinki,
    http://www-users.cs.york.ac.uk/~jc/research/uai08/ ,
    ID article: 2964.

    Abstract:
    The problem of learning discrete Bayesian networks from data is encoded as a weighted MAX-SAT problem and the MaxWalkSat local search algorithm is used to address it. For each dataset, the per-variable summands of the (BDeu) marginal likelihood for different choices of parents (`family scores') are computed prior to applying MaxWalkSat. Each permissible choice of parents for each variable is encoded as a distinct propositional atom and the associated family score encoded as a `soft' weighted single-literal clause. Two approaches to enforcing acyclicity are considered: either by encoding the ancestor relation or by attaching a total order to each graph and encoding that. The latter approach gives better results. Learning experiments have been conducted on 21 synthetic datasets sampled from 7 BNs. The largest dataset has 10,000 datapoints and 60 variables producing (for the `ancestor' encoding) a weighted CNF input file with 19,932 atoms and 269,367 clauses. For most datasets, MaxWalkSat quickly finds BNs with higher BDeu score than the `true' BN. The effect of adding prior information is assessed. It is further shown that Bayesian model averaging can be effected by collecting BNs generated during the search.


  104. Ioannis Korkontzelos, Ioannis Klapaftis and Suresh Manandhar. August 2008. Reviewing and Evaluating Automatic Term Recognition Techniques. Proceedings of the 6th International Conference on Natural Language Processing, GoTAL 2008, Gothenburg, Sweden,
    http://www-users.cs.york.ac.uk/~johnkork/pub/KorkontzelosEtAl-GoTAL-08-paper.pdf ,
    ID article: 2945.

    Abstract:
    Automatic Term Recognition (ATR) is defined as the task of identifying domain specific terms from technical corpora. Termhood-based approaches measure the degree that a candidate term refers to a domain specific concept. Unithood-based approaches measure the attachment strength of a candidate term constituents. These methods have been evaluated using different, often incompatible evaluation schemes and datasets. This paper provides an overview and a thorough evaluation of state-of-the-art ATR methods, under a common evaluation framework, i.e. corpora and evaluation method. Our contributions are two-fold: (1) We compare a number of different ATR methods, showing that termhood-based methods achieve in general superior performance. (2) We show that the number of independent occurrences of a candidate term is the most effective source for estimating term nestedness, improving ATR performance.


  105. B. Ziólko, S. Manandhar, R. C. Wilson and M. Ziólko. 2008. Semantic Modelling for Speech Recognition. Proceedings of Speech Analysis, Synthesis and Recognition. Applications in Systems for Homeland Security, Piechowice,
    ID article: 2958.

    Abstract:
    A new method of semantic modelling for speech recognition is presented. The method has some similarities to latent semantic analysis, but it gave better experimental results, which are provided as percentage of correctly recognised sentences from a corpus. The main difference is a choice of similar topics influencing a matrix describing probability of words appearing in topics.


  106. Alan M. Frisch, Warwick Harvey, Christopher Jefferson and Bernadette Martínez~Hernández. 2008. Essence: A Constraint Language for Specifying Combinatorial Problems. Constraints, 13(3):268--306,
    http://www.cs.york.ac.uk/aig/constraints/AutoModel/essence.pdf ,
    ID article: 3290


  107. C. De Lucia and M. Bartlett. 2008. Environmental Taxation in an Enlarged Europe: A Regional Perspective. Proceedings of the 29th Italian Association of Regional Science Annual Scientific Conference (AISRe 2008),
    http://www.cs.york.ac.uk/aig/papers/De_Lucia_2008.pdf ,
    ID article: 3325.

    Abstract:
    The recent enlargement of the European Union brings many opportunities, but also presents many challenges. While some regions and industries are likely to experience welfare gains and increased turnover respectively, others will likely find themselves net losers in this new system. One particularly relevant issue at the current time is that of the environment. This is understandable given the substantial consequences of movements of goods and pollution across Europe. Broad differences in trade patterns and environmental policy still exist between European countries (in particular between those of the existing states and those of the new accession countries) meaning that environmental and trade policies can influence the structure of whole economies and emissions levels. Of particular concern in the context of European enlargement is the idea of leakage of heavy industries to the new accession countries where labour costs are lower, but industry is typically more polluting. This paper therefore examines the effects should measures be taken to anticipate and avert such an increase in pollution, specifically through the introduction of a tax on various pollutants, harmonised at the European level. As the European Emissions Trading Scheme is already in place to deal with greenhouse gas emissions within Europe, we instead focus on other pollutants, namely Nitrogen Oxides (NOx) and Sulphur Dioxide (SO2). These are not global in nature as are greenhouse gases, but rather have also localised effects. There are many techniques that could be utilised for such a study, but this paper employs one of the most comprehensive. CGE modelling is a three stage process for analysing the potential impacts of policy changes or other economic shocks to a system. At the first stage, economic parameters (such as the substitutability of imported goods for domestically produced and goods, the substitutability between polluting and non-polluting input factors of production) are estimate


  108. Hodhod R. and Kudenko D. August 2008. Towards Intelligent Educational Interactive Narrative. In proceedings of Narrative in Interactive Learning Environments 2008 Conference: NILE2008, Edinburgh, Scotland,
    ID article: 2982


  109. H. Barber and D. Kudenko. December 2008. Generation of Dilemma-based Narratives: Method and Turing Test Evaluation. proceedings of the 1^st Joint International Conference on Interactive Digital Storytelling ICIDS08, Erfurt, Germany,
    ID article: 3074


  110. Marek Grzes and Daniel Kudenko. 2008. Multigrid Reinforcement Learning with Reward Shaping. Proceedings of the 18th International Conference on Artificial Neural Networks (ICANN'08), Springer-Verlag, LNCS,
    Yorkcategory: D,
    ID article: 3060


  111. Ed: Alan M. Frisch and Ian Miguel. 2008. Constraints: Special Issue on Abstraction and Automation in Constraint Modelling, Ed: Alan M. Frisch and Ian Miguel, Springer, 13(3):227--406,
    ID article: 2989


  112. Ed: Alan M. Frisch and Ian Miguel. 2008. Special Issue on Abstraction and Automation in Constraint Modelling, Ed: Alan M. Frisch and Ian Miguel, Springer, 13(3):227--406,
    ID article: 3291


  113. Yuan, T. and Schulze, J. 2008. Arg!Draw-An Argument Graphs Drawing Tool, Second International Conference on Computational Models of Argument (Software Demos),
    Yorkcategory: E - other reports, unrefereed papers, yellow report etc.,
    http://www.irit.fr/comma08/accepted.html#demos ,
    ID article: 3026


  114. Yuan, T., Moore, D. and Grierson, A.. 2008. A Human-Computer Dialogue System for Educational Debate, A Computational Dialectics Approach. International Journal of Artificial Intelligence in Education, 18(1):3-26,
    Yorkcategory: C - refereed journal paper,
    http://ihelp.usask.ca/iaied/ijaied/abstract/Vol_18/Yuan08.html ,
    ID article: 3218


  115. M. G. Ceddia, M. Bartlett and C. Perrings. 2008. Policies for the regulation of coexistence between GM and conventional crops. 12th Congress of the European Association of Agricultural Economists,
    http://www.cs.york.ac.uk/aig/papers/Ceddia_2008.pdf ,
    ID article: 3323.

    Abstract:
    Pollen-mediated gene flow is one of the main concerns associated with the introduction of genetically modified (GM) crops, since growers of GM varieties normally do not take into account its possible impact on conventional and organic growers therefore generating negative externalities. Should a premium for non-GM varieties emerge on the market, 'contamination' with GM pollen would generate a revenue loss for growers of non-GM varieties. The existence of such externalities has led the European Union (EU) to put forward the concept of coexistence in order to guarantee farmers' freedom to plant both conventional and GM varieties without generating economic losses to conventional farmers. The first part of this paper develops a simple economic model analysing the problem of pollen-mediated gene flow as a particular kind of production externality. The model, although simple, provides useful insights into the policy needed to regulate coexistence. Since pollen-mediated gene flow is distancedependent, the externalities will depend on the spatial structure of GM adoption in the landscape. The second part of the paper, taking GM herbicide tolerant oilseed rape (Brassica napus) as a model crop, uses a Monte Carlo experiment to generate data and then estimate the effect of some important policy variables (i.e. number of GM and conventional fields in the landscape, width of buffer zones and spatial aggregation) on the magnitude of the externality associated with pollen-mediated gene flow. Our results show that buffer areas on conventional fields are more effective than those on GM fields and that the degree of spatial aggregation exerts the largest marginal effect on the externality to conventional growers. The implications of the results for the coexistence policies in the EU are then discussed.


  116. Alan M. Frisch, Brahim Hnich, Zeynep Kiziltan and Ian Miguel. 2008. Filtering Algorithms for Multiset Ordering Constraints. Artificial Inteligence, 173(2):299-328,
    http://arxiv.org/abs/0903.0460 ,
    ID article: 2987


  117. Marek Grzes and Daniel Kudenko. 2008. Learning Potential for Reward Shaping in Reinforcement Learning with Tile Coding. Proceedings of the AAMAS'08 Workshop on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS-ALAg'08):17--23,
    Yorkcategory: D,
    ID article: 3058


  118. Amer Alzaidi and Dimitar Kazakov. 2008. Designing a Supply Chain Management Approach for Islamic Banking Using Reinforcement Learning with Multi-Agents Technology. The Saudi International Innovation Conference, Leeds, SIIC,
    ID article: 3071


  119. M. Arinbjarnar. January 2008. Dynamic Plot Generation Engine. In Proceedings of the Workshop on Integrating Technologies for Interactive Stories, Playa del Carmen, Mexico,
    http://www-users.cs.york.ac.uk/~maria/greinar/DynamicPlotGeneratingEngine.pdf ,
    ID article: 2715


  120. Bartosz Ziolko, Suresh Manandhar, Richard C. Wilson and Mariusz Ziolko. 2008. Language Model based on POS Tagger. SIGMAP:177-180,
    http://www-users.cs.york.ac.uk/~suresh/papers/SIGMAP_LMB_POS.pdf ,
    ID article: 3176


  121. Dimitar Kazakov and Ahmad R. Shahid. December 2008. Extracting Multilingual Dictionaries for the Teaching of CS and AI. 4th UK Workshop on AI in Education, Cambridge,
    http://www-users.cs.york.ac.uk/~ahmad/index.html ,
    ID article: 2961


  122. Hodhod R. and Kudenko D. June 2008. Educational Interactive Narrative for Ill Defined Domain. In proceedings of young researchers track (YRT). Held at The 9th International Conference on Intelligent Tutoring Systems (ITS’08), Montreal, Canada,
    ID article: 2983


  123. Yuan, T., Schulze, j., Devereux, J. and Reed, C.. 2008. Towards an Arguing Agents Competition: Building on Argumento, In Proceedings of IJCAI'2008 Workshop on Computational Models of Natural Argument, Patras, Greece,
    Yorkcategory: D - refereed international conference paper,
    http://www.cmna.info/CMNA8/programme/CMNA8-Yuan-etal.pdf ,
    ID article: 3023


  124. Marek Grzes and Daniel Kudenko. 2008. Robustness Analysis of SARSA(lambda): Different Models of Reward and Initialisation. Proceedings of the 13th International Conference on Artificial Intelligence: Methodology, Systems, Applications, Springer-Verlag, LNCS,
    Yorkcategory: D,
    ID article: 3061


  125. Andrews, Pierre, Manandhar, Suresh and De Boni, Marco. June 2008. Argumentative Human Computer Dialogue for Automated Persuasion. Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue, Association for Computational Linguistics:138--147, Columbus, Ohio,
    http://www.aclweb.org/anthology/W/W08/W08-0123 ,
    ID article: 3137


  126. Ioannis P. Klapaftis and Suresh Manandhar. July 2008. Word Sense Induction Using Graphs of Collocations. Proceedings of the 18th European Conference On Artificial Intelligence (ECAI-2008), IOS Press, Patras, Greece,
    http://www-users.cs.york.ac.uk/~suresh/papers/WSIUGOC.pdf ,
    ID article: 2934.

    Abstract:
    Word Sense Induction (WSI) is the task of identifying the different senses (uses) of a target word in a given text. Traditional graph-based approaches create and then cluster a graph, in which each vertex corresponds to a word that co-occurs with the target word, and edges between vertices are weighted based on the cooccurrence frequency of their associated words. In contrast, in our approach each vertex corresponds to a collocation that co-occurs with the target word, and edges between vertices are weighted based on the co-occurrence frequency of their associated collocations. A smoothing technique is applied to identify more edges between vertices and the resulting graph is then clustered. Our evaluation under the framework of SemEval-2007 WSI task shows the following: (a) our approach produces less sense-conflating clusters than those produced by traditional graph-based approaches, (b) our approach outperforms the existing state-of-the-art results.


  127. Servin, A. and Kudenko, D.. 2008. Multi-Agent Reinforcement Learning for Intrusion Detection: A Case Study and Evaluation. Proceedings of the 6th German conference on Multiagent System Technologies:159--170, Springer,
    ID article: 3046


  128. Enda Ridge and Edward Curry. 2007. A Roadmap of Nature-Inspired Systems Research and Development. Multi-Agent and Grid Systems, 3(1),
    ID article: 2700.

    Abstract:
    Nature-inspired algorithms such as genetic algorithms, particle swarm optimisation and ant colony algorithms have successfully solved computer science problems of search and optimisation. The initial implementations of these techniques focused on static problems solved on single machines. These have been extended by adding parallelisation capabilities in the vein of distributed computing with a centralised master/slave approach. However, the natural systems on which nature-inspired algorithms are based possess many additional characteristics that are of potential benefit within computing environments. In this paper, we discuss the benefits of nature-inspired techniques within modern and emerging computing environments. Software entities within these environments execute and interact in a fashion that is parallel, asynchronous, and decentralised. Given that the natural environment is in itself parallel, asynchronous and decentralised, nature-inspired techniques are an excellent fit for computing environments that exhibit these characteristics. Future research challenges for nature-inspired techniques within emerging computing environments are also discussed.


  129. R. Alfred, E. Paskaleva, D. Kazakov and M. Bartlett. 2007. Hierarchical Agglomerative Clustering of English-Bulgarian Parallel Corpora. Proceedings of the International Conference on Recent Advances in Natural Languages Processing (RANLP 2007):24-29,
    http://www.cs.york.ac.uk/aig/papers/Alfred_2007a.pdf ,
    ID article: 3328.

    Abstract:
    Most multilingual parallel corpora have become an essential resource for work in multilingual natural language processing. In this article, we report on our work using the hierarchical agglomerative clustering (HAC) technique to cluster multilingual parallel text on web contents. A clustering algorithm taking constraints from parallel corpora potentially has several attractive features. Firstly, training samples in another language provide indirect evidence for a classification or clustering result. Secondly, constraints from both languages may help to eliminate some biased language-specific usages, resulting in classes of better quality. Finally, the alignment between pairs of clustered documents can be used to extract words from each language, which may then be used for other applications, as an example in this paper, we utilise these words for term reduction. We explain the findings that we obtain from the clustering of a significant parallel corpus for a low-density and high-density of paired language, English and Bulgarian. Preliminary results show that the HAC algorithm can effectively cluster bilingual parallel corpora separately and still produce the same extracted words that best describe these clusters for both English and Bulgarian corpora.


  130. Yuan, T. Moore, D and Reed, C.. 2007. Computational Use of Informal Logic Dialogue Games. 20th Anniversary of the University of Akureyri, Ed: Óskarsson, H., University of Akureyri:345-366,
    Yorkcategory: B - part of book,
    http://babbage.computing.dundee.ac.uk/chris/publications/2007/yuan-etal2007.pdf ,
    ID article: 3016


  131. Ed: Enda Ridge and Edward Curry and Daniel Kudenko and Dimitar Kazakov. 2007. Nature-Inspired Systems for Parallel, Asynchronous and Decentralised Environments, Ed: Enda Ridge and Edward Curry and Daniel Kudenko and Dimitar Kazakov, IOS Press, Multi-Agent and Grid Systems, 3,
    ID article: 2701


  132. R. Alfred. 2007. The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining. ADMA:214-226,
    ID article: 2711


  133. Alan M. Frisch, Matthew Grum, Christopher Jefferson and Bernadette Martínez~Hernández. 2007. The Design of Essence: A Language for Specifying Combinatorial Problems. Proc. of the 20th International Joint Conference on Artificial Intelligence,
    http://www.cs.york.ac.uk/aig/constraints/AutoModel/design-of-essence.pdf ,
    ID article: 2245


  134. Enda Ridge and Daniel Kudenko. 2007. Analyzing Heuristic Performance with Response Surface Models: Prediction, Optimization and Robustness. Proceedings of the Genetic and Evolutionary Computation Conference, Ed: Dirk Thierens and Hans-Georg Beyer and Mauro Birattari and Josh Bongard and Jurgen Branke and John A. Clark and David Cliff and Clare B. Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moor, ACM, 1:150-157,
    ID article: 2704.

    Abstract:
    This research uses a Design of Experiments (DOE) approach to build a predictive model of the performance of a combinatorial optimization heuristic over a range of heuristic tuning parameter settings and problem instance characteristics. The heuristic is Ant Colony System (ACS) for the Travelling Salesperson Problem. 10 heurstic tuning parameters and 2 problem characteristics are considered. Response Surface Models (RSM) of the solution quality and solution time predicted ACS performance on both new instances from a publicly available problem generator and new real-world instances from the TSPLIB benchmark library. A numerical optimisation of the RSMs is used to find the tuning parameter settings that yield optimal performance in terms of solution quality and solution time. This paper is the first use of desirability functions, a well-established technique in DOE, to simultaneously optimise these conflicting goals. Finally, overlay plots are used to examine the robustness of the performance of the optimised heuristic across a range of problem instance characteristics. These plots give predictions on the range of problem instances for which a given solution quality can be expected within a given solution time.


  135. Heather Barber and Daniel Kudenko. June 2007. A User Model for the Generation of Dilemma-based Interactive Narratives, In Proceedings of the Artificial Intelligence and Interactive Digital Entertainment conferance, Optimizing Player Satisfaction technical report, Stanford, California,
    http://www-users.cs.york.ac.uk/~hmbarber/aiideworkshop.pdf ,
    ID article: 2725


  136. Bartosz Ziolko, Suresh Manandhar and Richard Wilson. October 2007. Fuzzy Recall and Precision for Speech Segmentation Evaluation. Proceedings of 3rd Language & Technology Conference, Poznan, Poland,
    http://www-users.cs.york.ac.uk/~suresh/papers/FRAPFSSE.pdf ,
    ID article: 2938


  137. Yuan, T., Svansson, V., Moore, D. and Grierson, A.. 2007. A Computer Game for Abstract Argumentation, In Proceedings of IJCAI'2007 Workshop on Computational Models of Natural Argument:62-68, Hyderabad, India,
    Yorkcategory: D - refereed international conference paper,
    http://cmna.csc.liv.ac.uk/CMNA7/papers/Yuan.pdf ,
    ID article: 3025


  138. James Cussens. 2007. Bayesian classification and regression trees. Expert Update, 9(3):37--42,
    ID article: 2680


  139. R. Alfred and D. Kazakov. 2007. Clustering Approach to Generalised Pattern Identification Based on Multi-Instanced Objects with DARA. ADBIS,
    ID article: 2714


  140. Ioannis P. Klapaftis and Suresh Manandhar. June 2007. UOY: A Hypergraph Model For Word Sense Induction & Disambiguation. Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007), Association for Computational Linguistics:414--417, Prague, Czech Republic,
    http://www.aclweb.org/anthology/S/S07/S07-1092.pdf ,
    ID article: 3141


  141. M. Arinbjarnar. November 2007. Rational Dialog in Interactive Games. AAAI 2007 Fall Symposium on Intelligent Narrative Technologies,
    http://www-users.cs.york.ac.uk/~maria/greinar/[Ari07].pdf ,
    ID article: 2691


  142. Rania Hodhod and Daniel Kudenko. October 2007. Interactive Narrative for Adaptive Educational Games. In Proceedings of YDS’07: The First York Doctoral Symposium on Computing, University of York, UK,
    ID article: 2728


  143. Silvia Quarteroni, Alessandro Moschitti, Suresh Manandhar and Roberto Basili. April 2007. Advanced Structural Representations for Question Classification and Answer Re-ranking. In: Proceedings of the European Conference on Information Retrieval (ECIR), Springer LNCS,
    http://www.springerlink.com/index/27xj64184r080326.pdf ,
    ID article: 3143


  144. Silvia Quarteroni and Suresh Manandhar. September 2007. User Modelling for Personalized Question Answering. Proceedings of AI*IA 2007. Rome, Italy,
    http://www-users.cs.york.ac.uk/~suresh/papers/UMFPQA.pdf ,
    ID article: 2852


  145. Enda Ridge and Daniel Kudenko. 2007. An Analysis of Problem Difficulty for a Class of Optimisation Heuristics. Proceedings of the Seventh European Conference on Evolutionary Computation in Combinatorial Optimisation, Ed: C. Cotta and J. Van Hemert, Springer-Verlag, Lecture Notes in Computer Science, 4446:198-209,
    ID article: 2702.

    Abstract:
    This paper investigates the effect of the cost matrix standard deviation of Travelling Salesman Problem (TSP) instances on the performance of a class of combinatorial optimisation heuristics. Ant Colony Optimisation (ACO) is the class of heuristic investigated. Results demonstrate that for a given instance size, an increase in the standard deviation of the cost matrix of instances results in an increase in the difficulty of the instances. This implies that for ACO, it is insufficient to report results on problems classified only by problem size, as has been commonly done in most ACO research to date. Some description of the cost matrix distribution is also required when attempting to explain and predict the performance of these algorithms on the TSP.


  146. R. Alfred, E. Paskaleva, D. Kazakov and M. Bartlett. 2007. Hierarchical Agglomerative Clustering for Cross-Language Information Retrieval. International Journal of Translation, 19(1),
    http://www.cs.york.ac.uk/aig/papers/Alfred_2007.pdf ,
    ID article: 3329.

    Abstract:
    In this article, we report on our work on applying hierarchical agglomerativeclustering (HAC) to a large corpus of documents where each appears both in Bulgarian and English. We cluster these documents for each language and compare the results both with respect to the shape of the tree and content of clusters produced. Clustering multilingual corpora provides us with an insight into the differences between languages when term frequency-based informationretrieval (IR) tools are used. It also allows one to use the natural language processing (NLP) and IR tools in one language to implement IR for another language. For instance, in this way, the most relevant articles to be translated from language X to language Y can be selected after studying the clusters of abstracts in language Y.


  147. Heather Barber and Daniel Kudenko. August 2007. Adaptive Generation of Dilemma-based Interactive Narratives. In the Advanced Intelligent Paradigms in Computer Games Series: Studies in Computational Intelligence, 71,
    ID article: 2723


  148. Yuan, T., Moore, D. and Grierson, A.. 2007. A Human Computer Debating System and Its Dialogue Strategies. International Journal of Intelligent Systems, Special Issue on Computational Models of Natural Argument, 22(1):133-156,
    Yorkcategory: C - refereed journal paper,
    http://www3.interscience.wiley.com/journal/113490135/abstract ,
    ID article: 3014


  149. R. Alfred and D. Kazakov. 2007. Discretisation Numbers for Multiple-Instances Problem in Relational Database. ADBIS:55-65,
    ID article: 2712


  150. Enda Ridge and Daniel Kudenko. April 2007. Screening the Parameters Affecting Heuristic Performance, Technical Report, The Department of Computer Science, The University of York,
    ID article: 2705


  151. Barnaby Fisher and James Cussens. 2007. Inductive Mercury Programming. Inductive Logic Programming: Proceedings of the 16th International Conference (ILP-06), Ed: Stephen Muggleton and Ramon Otero, Springer, Santiago de Compostela,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/ilp06.pdf ,
    ID article: 2683.

    Abstract:
    We investigate using the Mercury language to implement and design ILP algorithms, presenting our own ILP system IMP. Mercury provides faster execution than Prolog. Since Mercury is a purely declarative language, run-time assertion of induced clauses is prohibited. Instead IMP uses a problem-specific interpreter of ground representations of induced clauses. The interpreter is used both for cover testing and bottom clause generation. The Mercury source for this interpreter is generated automatically from the user's background knowledge using MOOSE, a Mercury parser generator. Our results include some encouraging results on IMP's cover testing speed, but overall IMP is still generally a little slower than ALEPH.


  152. Marco Chiarandini, Luís Paquete, Mike Preuss and Enda Ridge. 2007. Experiments on Metaheuristics: Methodological Overview and Open Issues,
    ID article: 2695.

    Abstract:
    Metaheuristics are a wide class of solution methods that have been successfully applied to many optimization problems. The assessment of these methods is commonly based on experimental analysis but the lack of a methodology in these analyses limits the scientific value of their results. In this paper we formalize different scenarios for the analysis and comparison of metaheuristics by experimentation. For each scenario we give pointers to the existing statistical methodology for carrying out a sound analysis. Finally, we provide a set of open issues and further research directions.


  153. Heather Barber and Daniel Kudenko. April 2007. Interactive Generation of Dilemma-based Narratives. In Proceedings of the Narrative AI and Intelligent Serious Games for Education, Newcastle,
    http://www-users.cs.york.ac.uk/~hmbarber/aisb07.pdf ,
    ID article: 2726


  154. Bartosz Ziolko, Suresh Manandhar and Richard Wilson. October 2007. Triphone Statistics for Polish Language. Proceedings of 3rd Language & Technology Conference, Poznan, Poland,
    http://www.springerlink.com/content/ggu6415448452p5m/ ,
    ID article: 3140


  155. James Cussens. 2007. Model Equivalence of PRISM programs. Proceedings of the Dagstuhl seminar: Probabilistic, Logical and Relational Learning - A Further Synthesis,
    http://kathrin.dagstuhl.de/files/Submissions/07/07161/07161.CussensJames1.Paper!!.pdf ,
    ID article: 2681.

    Abstract:
    The problem of deciding the probability model equivalence of two PRISM programs is addressed. In the finite case this problem can be solved (albeit slowly) using techniques from algebraic statistics, specifically the computation of elimination ideals and Gröbner bases. A very brief introduction to algebraic statistics is given. Consideration is given to cases where shortcuts to proving/disproving model equivalence are available.


  156. Enda Ridge. 2007. Design of Experiments for the Tuning of Optimisation Algorithms, PhD, The Department of Computer Science, The University of York,
    ID article: 2699


  157. Enda Ridge, Thomas Stützle, Mauro Birattari and Holger Hoos. 2007. SLS-DS 2007: Doctoral Symposium on Engineering Stochastic Local Search Algorithms, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium,
    ID article: 2710.

    Abstract:
    The inaugural Doctoral Symposium on Engineering Stochastic Local Search Algorithms (SLS-DS) was held at the Université Libre de Bruxelles, Belgium on 7 September 2007 jointly with the SLS 2007 Workshop. SLS-DS is a forum for doctoral students to present their work and obtain guidance from fellow researchers as well as to provide contact with other students at a similar stage in their careers. The symposium exposes students to helpful criticism before their thesis defence, and fosters discussions related to future career perspectives. The symposium consists of a series of short presentations followed by a poster session. The papers in these proceedings were selected based on relevance, quality and clarity of presentation. They provide a useful guide to emerging research and new trends in the stochastic local search field. The topics covered include: - Methodological developments for the implementation of SLS algorithms. - Experimental studies of SLS algorithms (behaviour of SLS algorithms, comparison of SLS algorithms, ...), problem characteristics and their impact on algorithm performance. -Case studies in the development of well designed SLS algorithms. -Aspects that become relevant when moving from classical


  158. Moschitti, Alessandro, Quarteroni, Silvia, Basili, Roberto and Manandhar, Suresh. June 2007. Exploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, Association for Computational Linguistics:776--783, Prague, Czech Republic,
    http://www.aclweb.org/anthology/P07-1098.pdf ,
    ID article: 3142


  159. Suresh Manandhar and Silvia Quarteroni. 2007. A Chatbot-based Interactive Question Answering System. In Proceedings of the 11th Workshop on the Semantics and Pragmatics of Dialogue (DECALOG),Trento, Italy:83 - 90,
    http://www-users.cs.york.ac.uk/~suresh/papers/ChatBot_DECALOG.pdf ,
    ID article: 3200


  160. Rania Hodhod. April 2007. Overview of Shortcomings and Proposed Solutions. In proceedings of AISB'07: Artificial and Ambient Intelligence, Newcastle University, UK,
    ID article: 2729


  161. Enda Ridge and Daniel Kudenko. 2007. Screening the Parameters Affecting Heuristic Performance. Proceedings of the Genetic and Evolutionary Computation Conference, Ed: Dirk Thierens and Hans-Georg Beyer and Mauro Birattari and Josh Bongard and Jurgen Branke and John A. Clark and David Cliff and Clare B. Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moor, ACM, 1,
    ID article: 2703.

    Abstract:
    This research screens the tuning parameters of a combinatorial optimization heuristic. Specifically, it presents a Design of Experiments (DOE) approach that uses a Fractional Factorial Design to screen the tuning parameters of Ant Colony System (ACS) for the Travelling Salesperson problem. Screening is a preliminary step to building a Response Surface Model (RSM) [20, 18]. It identifies those parameters that need not be included in a Response Surface Model, thus reducing the complexity and expense of the RSM design. 10 algorithm parameters and 2 problem characteristics are considered. Open questions on the effect of 3 parameters on performance are answered. Ant placement and choice of ant for pheromone update have no effect. However, the choice of parallel or sequential solution construction does indeed influence performance. A further parameter, sometimes assumed important, was shown to have no effect on performance. A new problem characteristic that effects performance was identified. The importance of measuring solution time was highlighted by helping identify the prohibitive cost of non-integer parameters where those parameters are exponents in the ACS algorithm’s computations. All results are obtained with a publicly available algorithm and problem generator.


  162. Heather Barber and Daniel Kudenko. June 2007. Dynamic Generation of Dilemma-based Interactive Narratives. In Proceedings of the Artificial Intelligence and Interactive Digital Entertainment conferance, Gives a good overview of the GADIN system, Stanford, California,
    http://www-users.cs.york.ac.uk/~hmbarber/aiide07.pdf ,
    ID article: 2724


  163. M. G. Ceddia, M. Bartlett and C. Perrings. 2007. Landscape gene flow, coexistence and threshold effect: the case of genetically modified herbicide tolerant oilseed rape (Brassica napus). Ecological Modelling, Elsevier, 205(1):169-180,
    http://dx.doi.org/10.1016/j.ecolmodel.2007.02.025 ,
    ID article: 3321.

    Abstract:
    Globally there have been a number of concerns about the development of genetically modified crops many of which relate to the implications of gene flow at various levels. In Europe these concerns have led the European Union (EU) to promote the concept of ‘coexistence’ to allow the freedom to plant conventional and genetically modified (GM) varieties but to minimise the presence of transgenic material within conventional crops. Should a premium for non-GM varieties emerge on the market, the presence of transgenes would generate a ‘negative externality’ to conventional growers. The establishment of maximum tolerance level for the adventitious presence of GM material in conventional crops produces a threshold effect in the external costs. The existing literature suggests that apart from the biological characteristics of the plant under consideration (e.g. self-pollination rates, entomophilous species, anemophilous species, etc.), gene flow at the landscape level is affected by the relative size of the source and sink populations and the spatial arrangement of the fields in the landscape. In this paper, we take genetically modified herbicide tolerant oilseed rape (GM HT OSR) as a model crop. Starting from an individual pollen dispersal function, we develop a spatially explicit numerical model in order to assess the effect of the size of the source/sink populations and the degree of spatial aggregation on the extent of gene flow into conventional OSR varieties under two alternative settings. We find that when the transgene presence in conventional produce is detected at the field level, the external cost will increase with the size of the source area and with the level of spatial disaggregation. On the other hand when the transgene presence is averaged among all conventional fields in the landscape (e.g. because of grain mixing before detection), the external cost will only depend on the relative size of the source area. The model could readily be incorporated int


  164. R. Alfred and D. Kazakov. 2007. Aggregating Multiple Instances in Relational Database Using Semi-Supervised Genetic Algorithm-Based Clustering Technique. ADBIS,
    ID article: 2713


  165. Alan M. Frisch, Matthew Grum, Christopher Jefferson and Bernadette Martínez~Hernández. 2007. The Design of Essence: A Constraint Language for Specifying Combinatorial Problems. IJCAI-07,
    www.cs.york.ac.uk/aig/constraints/AutoModel/design-of-essence.pdf ,
    ID article: 2672


  166. Enda Ridge and Daniel Kudenko. 2007. Tuning the Performance of the MMAS Heuristic. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics, Ed: Thomas Stützle and Mauro Birattari and Holger Hoos, Springer, Lecture Notes in Computer Science, 4638:46-60, Berlin / Heidelberg,
    ID article: 2706.

    Abstract:
    This paper presents an in-depth Design of Experiments (DOE) methodology for the performance analysis of a stochastic heuristic. The heuristic under investigation is Max-Min Ant System (MMAS) for the Travelling Salesperson Problem (TSP). Specifically, the Response Surface Methodology is used to model and tune MMAS performance with regard to 10 tuning parameters, 2 problem characteristics and 2 performance metrics: solution quality and solution time. The accuracy of these predictions is methodically verified in a separate series of confirmation experiments. The two conflicting responses are simultaneously optimised using desirability functions. Recommendations on optimal parameter settings are made. The optimal parameters are methodically verified. The large number of degrees-of-freedom in the MMAS design are overcome with a Minimum Run Resolution V design. Publicly available algorithm and problem generator implementations are used throughout. The paper should therefore serve as an illustrative case study of the principled engineering of a stochastic heuristic.


  167. James Cussens. 2007. Logic-based Formalisms for Statistical Relational Learning. Introduction to Statistical Relational Learning, Ed: Lise Getoor and Ben Taskar, MIT Press, Cambridge, MA,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/srl-book.pdf ,
    ID article: 2682


  168. Matthew Grounds and Daniel Kudenko. 2007. Parallel Reinforcement Learning with Linear Function Approximation. International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS),
    ID article: 2735


  169. Ioannis Korkontzelos, Andreas Vlachos and Ian Lewin. 2007. From Gene Names to Actual Genes. Proceedings of ISMB BioLink SIG on Text Data Mining, Vienna, Austria,
    http://www.cl.cam.ac.uk/~il220/papers/biolink07.pdf ,
    ID article: 2721.

    Abstract:
    A common task in biomedical text mining is the recognition of gene names. In many applications though, it is important to know whether a gene name refers to the actual gene or to an entity related to it. This paper presents a trainable system to perform this task. It combines syntactic parsing with SVMs and achieves 78.63% accuracy. The training data used were generated automatically by a simple rule-based tagger. Such an approach can be useful to other fields which exhibit similar ambiguity in the way names are used to refer to entities.


  170. Arturo Servin and Daniel Kudenko. 2007. Multi-Agent Reinforcement Learning for Intrusion Detection. Seventh Symposium on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS),
    ID article: 2736


  171. D. Kazakov. December 2006. Open Book Examinations in AI Teaching: A Case Study.. Proc. of the Second UK Workshop on AI in Education,
    ID article: 2255


  172. R. Alfred and D. Kazakov.. August 2006. Data Summarization Approach to Relational Domain Learning Based on Frequent Pattern to Support the Development of Decision Making.. In the Proceedings of The Second International Conference of Advanced Data Mining and Applications, (ADMA 2006),
    ID article: 2262


  173. Andrews, Pierre, Manandhar, Suresh and De Boni, Marco. 2006. The 19th International FLAIRS Conference. The 19th International FLAIRS Conference,
    http://www-users.cs.york.ac.uk/~suresh/papers/IEIPDAMRF.pdf ,
    ID article: 2863.

    Abstract:
    Human computer dialogue systems -- despite being the subject of a long research -- are limited to a few restricted domains and are still considered austere by their users. There is evidence that humans act differently when engaged in computer dialogue than during human to human dialogue [Shechtman03Media]. This is because dialogue systems do not take into account aspects contributing to the natural effect of human to human conversation, such as emotions and social cues. Our current research focuses on using human-computer dialogue for health-care counselling. In particular, we are developing a dialogue system that should be capable of changing the user health behaviour based on techniques of persuasion and argumentation. In our opinion, natural argumentation -- especially persuasive argumentation -- to show empathy and use social cues to be effective [andrews06persuasive]. We describe here the design of a multi layer framework to separate the persuasion planning and the management of surface-level dialogue cues.


  174. M. Bartlett. 2006. Language as an Exaptation: Simulating the Origin of Syntax, Department of Computer Science, University of York,
    http://www.cs.york.ac.uk/aig/papers/Bartlett_2006.pdf ,
    ID article: 3335.

    Abstract:
    Much of the recent computational research into the evolution of language has concentrated on explaining the origins of compositionality and syntax in language. In such models, the ability of syntax to allow generalisation leads to it naturally emerging in the resulting language to capture structural properties of the semantic space under discussion. However, while such models can explain why protolanguages may have gained in structural complexity to become fully-fedged languages given the opportunity, they do not explain how the ability of individuals to handle composition of linguistic fragments evolved: while existing models may explain the emergence of syntax in the language, they presuppose a syntax-handling capability in the brain. It is the evolution of this capability that this research seeks to address. This thesis lays out one possible explanation for the evolution of this linguistic ability and develops from it a computational model to assess its feasibility. Specifically, the biologically plausible idea is examined that the ability to handle compositionality in language is derived from a similar, and earlier, ability to handle compositionality in navigation and that the same underlying neural mechanisms are used. A second, supplemental, theory is also proposed, that one of the original purposes of language may have been for use in navigation. Communication in this case would be a form of inherently cooperative social behaviour which could lead to evolutionary benefits for groups of individuals possessing this trait. To assess the ability of these theories to explain the evolution of the capability of individuals to handle compositional language, a multi-agent simulation is created in which populations of agents with a variety of linguistic and foraging policies are tested for their abilities to survive and reproduce. In addition to using the model to essay the relative successes of these behaviours, the role of the environment structure in deter


  175. Ioannis P. Klapaftis and Suresh Manandhar. 2006. Term Sense Disambiguation for Ontology Learning. ISDA '06: Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), IEEE Computer Society:844--849, Washington, DC, USA,
    http://www-users.cs.york.ac.uk/~suresh/papers/TSDFOL.pdf ,
    ID article: 2862


  176. A. M. Frisch, B. Hnich, Z. Kiziltan and I. Miguel and T. Walsh. 2006. Propagation Algorithms for Lexicographic Ordering Constraints. Artificial Inteligence, 170(10):803--834,
    ID article: 2252


  177. D. Kazakov.. November 2006. Extended fitness for evolutionary algorithms.. META'06 Workshop on Metaheuristics.,
    ID article: 2256


  178. Sypka, P., Ziolko, M. and Ziolko, B. 2006. Approach of JPEG2000 Compression Standard to Transmultiplexed Images. Proceedings of the Visualization, Imaging, and Image Processing, VIIP 2006,
    ID article: 2677


  179. P.Sypka, M. Ziólko and B. Ziólko. 2006. Lossless JPEG-base Compression of Transmultiplexed Images. Proceedings of the 12th Digital Signal Processing Workshop:531-534,
    ID article: 2271


  180. E. Ridge, D. Kudenko and D. Kazakov. 2006. Parallel, Asynchronous and Decentralised Ant Colony System. The First International Symposium on Nature-Inspired Systems for Parallel, Asynchronous and Decentralised Environments (NISPADE).,
    ID article: 2277


  181. Andrews, Pierre, De Boni, Marco and Manandhar, Suresh. March 2006. Persuasive Argumentation in Human Computer Dialogue. Proceedings of the AAAI 2006 Spring Symposium on Argumentation for Consumers of Healthcare, Stanford University, California,
    http://www.aaai.org/Papers/Symposia/Spring/2006/SS-06-01/SS06-01-002.pdf ,
    ID article: 3145.

    Abstract:
    In the field of natural language dialogue, a new trend is exploring persuasive argumentation theories. Applying these theories to human-computer dialogue management could lead to a more comfortable experience for the user and give way to new applications. In this paper, we study the different aspects of persuasive communication needed for health-care advising and how to implement them to produce efficient, computer directed persuasion. Our opinion is that a persuasive dialogue will have to combine the current logical approach to persuasion with novel emotional cues to render the dialogue more comfortable to the user.


  182. Sally Fincher, David Barnes, Peter Bibby and Jim Bown. August 2006. Some Good Ideas from the Disciplinary Commons.. The Higher Education Academy 7th Annual Conference.,
    ID article: 2260


  183. Bartosz Ziolko, S. Manandhar and R. C. Wilson. 2006. Phoneme segmentation of speech. Proceedings of 18th International Conference on Pattern Recognition,
    http://www-users.cs.york.ac.uk/~suresh/papers/PSOS.pdf ,
    ID article: 2930


  184. S. Quarteroni and S. Manandhar. 2006. Incorporating User Models in Question Answering to Improve Readability. Proceedings of KRAQ, Ed: F. Benamara and P. Saint-Dizier,
    http://www.aclweb.org/anthology/W/W06/W06-1809.pdf ,
    ID article: 3147


  185. Dimitar Kazakov, James Cussens and Suresh Manandhar. 2006. On The Duality of Semantics and Syntax: The PP Attachment Case, Department of Computer Science, University of York, UK,
    http://www-users.cs.york.ac.uk/~suresh/papers/OTDOSASTPAC.pdf ,
    ID article: 2865


  186. D. Kazakov and I. Bate. September 2006. Towards New Methods for Developing Real-Time Systems: Automatically Deriving Loop Bounds Using Machine Learning.. Proc. of the 11th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA),
    ID article: 2258


  187. R. Alfred and D. Kazakov.. June 2006. Pattern-Based Transformation Approach to Relational Domain Learning Using DARA.. The 2006 International Conference on Data Mining (DMIN'06).,
    ID article: 2264


  188. Andrews, Pierre, Manandhar, Suresh and De Boni, Marco. 2006. Integrating Emotions in Persuasive Dialogue: A Multi-Layer Reasoning Framework. The 19th International FLAIRS Conference,
    http://www-users.cs.york.ac.uk/~suresh/papers/IEIPDAMRF.pdf ,
    ID article: 2937.

    Abstract:
    Human computer dialogue systems -- despite being the subject of a long research -- are limited to a few restricted domains and are still considered austere by their users. There is evidence that humans act differently when engaged in computer dialogue than during human to human dialogue [Shechtman03Media]. This is because dialogue systems do not take into account aspects contributing to the natural effect of human to human conversation, such as emotions and social cues. Our current research focuses on using human-computer dialogue for health-care counselling. In particular, we are developing a dialogue system that should be capable of changing the user health behaviour based on techniques of persuasion and argumentation. In our opinion, natural argumentation -- especially persuasive argumentation -- to show empathy and use social cues to be effective [andrews06persuasive]. We describe here the design of a multi layer framework to separate the persuasion planning and the management of surface-level dialogue cues.


  189. D. Kazakov.. November 2006. Self-reflective Machine Learning Challenge.. META'06 Workshop on Metaheuristics.,
    ID article: 2257


  190. Alan M. Frisch, Matthew Grum, Christopher Jefferson and Bernadette Martínez~Hernández. September 2006. Why Essence? Frequently Asked Questions about a new Language Specifying Combinatorial Problems. Proc. of the 5th Int. Workshop on Constraint Modelling and Reformulation,
    http://www.cs.york.ac.uk/aig/constraints/AutoModel/faq.pdf ,
    ID article: 2250


  191. M. Bartlett and D. Kazakov. 2006. The Evolution of Syntactic Capacity From Navigational Ability. Proceedings of the 6th International Conference on the Evolution of Language (EVOLANG 2006), World Scientific Pub Co Inc:393–394,
    http://www.cs.york.ac.uk/aig/papers/Bartlett_2006a.pdf ,
    ID article: 3336


  192. Bernadette Martínez~Hernández and Alan M. Frisch. September 2006. The Automatic Generation of Redundant Representations and Channelling Constraints.. Proc. of the 5th Int. Workshop on Constraint Modelling and Reformulation,
    http://www.cs.york.ac.uk/aig/constraints/AutoModel/channelling-modelling06.pdf ,
    ID article: 2251


  193. Suresh Manandhar, S. Tarim and Toby Walsh. 2006. Stochastic Constraint Programming: A Scenario-Based Approach. Constraints, 11(1):53-80,
    http://www.springerlink.com/content/m10m396m4662v851/ ,
    ID article: 3148


  194. Ioannis P. Klapaftis and Suresh Manandhar. 2006. Unsupervised Word Sense Disambiguation Using The WWW. STAIRS'06: Proceedings of the Third Starting AI Researchers' Symposium, IOS PRess:174--183, Amsterdam, The Netherlands,
    http://www-users.cs.york.ac.uk/~suresh/papers/UWSDUTW.pdf ,
    ID article: 2864


  195. Enda Ridge and Daniel Kudenko. 2006. Sequential Experiment Designs for Screening and Tuning Parameters of Stochastic Heuristics. Workshop on Empirical Methods for the Analysis of Algorithms at the Ninth International Conference on Parallel Problem Solving from Nature, Ed: LuĂ­s Paquete and Marco Chiarandini and Dario Basso:27-34,
    ID article: 2253.

    Abstract:
    This paper describes a sequential experimentation approach for efficiently screening and tuning the parameters of a stochastic heuristic. Stochastic heuristics such as ant colony algorithms often use a large number of tuning parameters. Testing all combinations of these factors is prohibitive and inefficient. The sequential procedure recommended by this paper uses resolution IV fractional factorial designs with fold-over and centre points as an efficient way to screen the most important tuning parameters. The effects of the most important parameters are then modelled using a central composite design and optimised with standard numerical methods. All designs, their analyses and interpretation are illustrated using the Ant Colony System algorithm. The use of standard designs and methods has the benefit that the presented procedure can easily be followed with commercial software rather that relying on custom methodologies and tools that have only been developed in an academic context. Such a procedure has not been applied to ant colony algorithms before.


  196. Nicos Angelopoulos and James Cussens. 2006. Exploiting independence for branch operations in Bayesian learning of C&RTs. Probabilistic, Logical and Relational Learning - Towards a Synthesis, Ed: Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany, [date of citation: 2006-01-01], Dagstuhl Seminar Proceedings(5051), Dagstuhl, Germany,
    http://drops.dagstuhl.de/opus/volltexte/2006/415 [date of citation: 2006-01-01] ,
    ID article: 2684


  197. Bartosz Ziolko, J. Galka, Suresh Manandhar and Richard Wilson. 2006. The use of statistics of Polish phonemes in speech recognition. Speech Signal Annotation, Processing and Synthesis, Poznan,
    http://www-users.cs.york.ac.uk/~suresh/papers/TUOSOPPISR.pdf ,
    ID article: 2866


  198. S. Quarteroni and S. Manandhar. 2006. User Modelling for Adaptive Question Answering and Information Retrieval. Proceedings of FLAIRS, Ed: G. Sutcliffe and R. Goebel, AAAI Press,
    http://www-users.cs.york.ac.uk/~suresh/papers/UMFAQAAIR.pdf ,
    ID article: 2870


  199. Blythe M., Manandhar S., Wright P., and Gaver B.. 2006. The Literary Fridge: Books of the Moment and Digital Fridge Poetry.. First International Symposium on Culture, Creativity and Interaction Design. CCID 2006. Queen Mary, University of London,
    http://www-users.cs.york.ac.uk/~suresh/papers/YLFBOTMADFP.pdf ,
    ID article: 2947


  200. Lillian Clark, I-Hsien Ting, Chris Kimble and Peter Wright. 2006. Combining Ethnographic and Clickstream Data to Identify Browsing Strategies. Journal of Information Research, 11(2),
    http://informationr.net/ir/11-2/paper249.html ,
    ID article: 2249


  201. Quintin Cutts, Sally Fincher, David Barnes and Peter Bibby. August 2006. Laboratory Exams in First Programming Courses.. The Higher Education Academy 7th Annual Conference.,
    ID article: 2261


  202. R. Alfred and D. Kazakov. 2006. Weighted Pattern-Based Transformation Approach to Relational Data Mining. ICAIET,
    ID article: 2717


  203. Z. Lock and D. Kudenko. 2006. Interactions between Stereotypes. Adaptive Hypermedia and Adaptive Web-Based Systems (AH ’06).,
    ID article: 2275


  204. Andrews, Pierre, De Boni, Marco and Manandhar, Suresh. March 2006. Proceedings of the AAAI 2006 Spring Symposium on Argumentation for Consumers of Healthcare. Proceedings of the AAAI 2006 Spring Symposium on Argumentation for Consumers of Healthcare, Stanford University, California,
    http://www-users.cs.york.ac.uk/~suresh/papers/PAIHCD.pdf ,
    ID article: 2860.

    Abstract:
    In the field of natural language dialogue, a new trend is exploring persuasive argumentation theories. Applying these theories to human-computer dialogue management could lead to a more comfortable experience for the user and give way to new applications. In this paper, we study the different aspects of persuasive communication needed for health-care advising and how to implement them to produce efficient, computer directed persuasion. Our opinion is that a persuasive dialogue will have to combine the current logical approach to persuasion with novel emotional cues to render the dialogue more comfortable to the user.


  205. D. Kazakov and I. Bate.. August 2006. Learning Worst-Case Execution Time Loop Bounds with Inductive Logic Programming.. Proceedings of the 16th International Conference on Inductive Logic Programming (short papers),
    ID article: 2263


  206. R. Alfred and D. Kazakov.. June 2006. An Association-classification Hybrid Rule Learning Approach to Relational Data Mining.. The 2006 International Conference on Artificial Intelligence (ICAI'06).,
    ID article: 2265


  207. H. Amini, D. Kazakov and E. Ridge.. April 2006. Parallelism vs Communication Overhead Trade-off in a JADE Multi-Agent Implementation of Cellular Automata.. The First International Symposium on Nature-Inspired Systems for Parallel, Asynchronous and Decentralised Environments (NISPADE),
    ID article: 2266


  208. Ziolko, M., Sypka, P. and Ziolko, B. 2006. Application of 1-D Transmultiplexer to Images Transmission. Proceedings of the 32nd Annual Conference of the IEEE Industrial Electronics Society IECON-2006:3564-3567,
    ID article: 2676


  209. M. Grounds and D. Kudenko. 2006. Parallel Reinforcement Learning by Merging Function Approximations. Sixth European Workshop on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS’06).,
    ID article: 2276


  210. R. Alexander, D. Kazakov and T. Kelly.. September 2006. System of Systems Hazard Analysis using Simulation and Machine Learning.. In Proceedings of the 25th International Conference on Computer Safety, Reliability and Security SAFECOMP 2006,
    ID article: 2259


  211. Enda Ridge, Daniel Kudenko and Dimitar Kazakov. 2006. A Study of Concurrency in the Ant Colony System Algorithm. Proceedings of the IEEE Congress on Evolutionary Computation:1662-1669,
    ID article: 2254.

    Abstract:
    This paper reports the results of a study of a specific type of concurrency in the Ant Colony System (ACS) algorithm. Studies of Cellular Automata (CA) have shown that the update mechanism used can have a dramatic influence on the dynamics of the CA. ACS is usually implemented with a sequential update mechanism. A new method for controlling the concurrency in a nature-inspired algorithm is introduced. Comprehensive tests on a wide range of problem instances are reported. The study found that concurrency levels had no statistically significant effect on ACS performance. This result is interesting because it contradicts what has been observed in another form of nature-inspired algorithm, namely CAs.


  212. Björn Ţór Jónsson, Maria Arinbjarnar, Bjarnsteinn Ţórsson and Michael J. Franklin. 2006. Performance and overhead of semantic cache management. ACM Trans. Inter. Tech., ACM Press, 6(3):302--331, New York, NY, USA,
    http://doi.acm.org/10.1145/1151087.1151091 ,
    ID article: 2843


  213. Heather Barber and Daniel Kudenko. October 2006. Adaptive Generation of Dilemma-based Interactive Narratives. In Proceedings of the Adaptive Approaches for Optimising Player Satisfaction in Computer and Physical Games, Rome,
    http://www-users.cs.york.ac.uk/~hmbarber/drama06.pdf ,
    ID article: 2727


  214. Bartosz Ziolko, S. Manandhar, R. C. Wilson and M. Ziólko. 2006. Wavelet method of speech segmentation. Proceedings of 14th European Signal Processing Conference EUSIPCO,
    http://www-users.cs.york.ac.uk/~suresh/papers/WMOSS.pdf ,
    ID article: 2868


  215. S. Quarteroni and S. Manandhar. 2006. Adaptivity in Question Answering with User Modelling and a Dialogue Interface. Proceedings of EACL, Trento, Italy,
    http://www.aclweb.org/anthology-new/E/E06/E06-2029.pdf ,
    ID article: 3146


  216. I-Hsien Ting, Chris Kimble and Daniel Kudenko. 2005. A Pattern Restore Method for Restoring Missing Patterns in Server Side Clickstream Data. Proceedings of the 7th Asia-Pacific Web Conference:501--512, Shanghai, China,
    http://www.cs.york.ac.uk/mis/docs/LNCS-3399-2005.pdf ,
    ID article: 2290


  217. Alan M. Frisch and Bernadette Martínez-Hernández. 2005. The Systematic Generation of Channelling Constraints. Fourth International Workshop on Modelling and Reformulating Constraint Satisfaction Problems, Held at the 11th International Conference on Principles and Practice of Constraint Programming:89-101,
    ID article: 2301


  218. Silvia Quarteroni and Suresh Manandhar. September 2005. Adaptivity in Question Answering Using Dialogue Interfaces. Proceedings of the Workshop on Cultural Heritage - 9th Conference of the Italian Association for Artificial Intelligence (AI*IA 2005), Milan, Italy,
    http://www.aclweb.org/anthology-new/E/E06/E06-2029.pdf ,
    ID article: 3149


  219. Martin Carpenter and Daniel Kudenko. 2005. Baselines for Joint-Action Reinforcement Learning of Coordination in Cooperative Multi-Agent Systems. Adaptive Agents and Multi-Agent Systems II, Ed: Daniel Kudenko and Dimitar Kazakov and Eduardo Alonso, Springer LNAI 3394,
    ID article: 2284


  220. K. Simov, D. Kazakov and P. Osenova. 2005. Proceedings of the Workshop on Exploring Syntactically Annotated Corpora, (held in conjunction with the Corpus Linguistics 2005 conference, University of Birmingham, 14-17 July 2005), Department of Computer Science, University of York, UK,
    http://www.cs.york.ac.uk/ftpdir/reports/YCS-2005-392.pdf ,
    ID article: 2293


  221. Chris Jefferson and Alan M. Frisch. 2005. Representations of Sets and Multisets in Constraint Programming. Fourth International Workshop on Modelling and Reformulating Constraint Satisfaction Problems, Held at the 11th International Conference on Principles and Practice of Constraint Programming:102-116,
    ID article: 2300


  222. D. Kazakov and M. Sweet. 2005. Evolving the Game of Life. Adaptive Agents and Multi-Agent Systems II, Ed: D. Kudenko and D. Kazakov and E. Alonso, Springer, Lecture Notes in Artificial Intelligence, 3394:132--146,
    http://www.springerlink.com/index/P863PPMG2XF435KQ ,
    ID article: 2294


  223. Xiao Song Lu and Daniel Kudenko. 2005. Reinforcement Learning in a Sensor-Evader Domain. Proceedings of the Fifth European Workshop on Adaptive Agents and Multi-Agent Systems,
    ID article: 2287


  224. Nicos Angelopoulos and James Cussens. 2005. Tempering for Bayesian C&RT. Proceedings of the 22nd International Conference on Machine Learning (ICML05):17--24, Bonn,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/icml05.pdf ,
    ID article: 2306.

    Abstract:
    This paper concerns the experimental assessment of tempering as a technique for improving Bayesian inference for C&RT models. Full Bayesian inference requires the computation of a posterior over all possible trees. Since exact computation is not possible Markov chain Monte Carlo (MCMC) methods are used to produce an approximation. C&RT posteriors have many local modes: tempering aims to prevent the Markov chain getting stuck in these modes. Our results show that a clear improvement is achieved using tempering.


  225. Thimal Jayasooriya and Suresh Manandhar. 2005. Lightweight natural language processing. Perspectives in Pervasive Computing.. Proceedings of the NTWM Interface Event, IEE,
    http://www-users.cs.york.ac.uk/~suresh/papers/LNLPPIPC.pdf ,
    ID article: 2876


  226. Yuan, T., Moore, D. and Grierson, A.. 2005. Development and Evaluation of a System for Educational Debate, In Proceedings of IJCAI'2005 Workshop on Computational Models of Natural Argument:12-17, Edinburgh UK,
    Yorkcategory: D - refereed international conference paper,
    http://www.computing.dundee.ac.uk/staff/creed/research/cmna2005/02yuan.pdf ,
    ID article: 3027


  227. Enda Ridge, Daniel Kudenko, Dimitar Kazakov and Edward Curry. 2005. Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments. Self-Organization and Autonomic Informatics, Ed: H. Czap and R. Unland and C. Branki and H. Tianfield, IOS Press, 1:35--49,
    ID article: 2296


  228. Alan M. Frisch, Matthew Grum, Chris Jefferson and Bernadette Martínez-Hernández. October 2005. The Essence of Essence. International Workshop on Modelling and Reformulating Constraint Satisfaction Problems, Ed: B. Hnich and P. Prosser and B. Smith, Held at the 11th International Conference on Principles and Practice of Constraint Programming:73-88,
    ID article: 2298


  229. M. Bartlett and D. Kazakov. 2005. Comparing resource sharing with information exchange in co-operative agents, and the role of environment structure. Adaptive Agents and Multi-Agent Systems II, Adaptation and Multi-Agent Learning, Springer, Lecture Notes in Artificial Intelligence, 3394:41-54,
    http://dx.doi.org/10.1007/b106974 ,
    ID article: 3339.

    Abstract:
    This paper presents a multi-agent system which has been developed in order to test our theories of language evolution. We propose that language evolution is an emergent behaviour, which is influenced by both genetic and social factors and show that a multi-agent approach is thus most suited to practical study of the salient issues. We present the hypothesis that the original function of language in humans was to share navigational information, and show experimental support for this hypothesis through results comparing the performance of agents in a series of environments. The approach, based loosely on the Songlines of Australian Aboriginal culture, combines individual exploration with exchange of information about resource location between agents. In particular, we study how the degree to which language use is beneficial varies with a particular property of the environment structure, that of the distance between resources needed for survival.


  230. Zoe Lock and Daniel Kudenko. 2005. Combining Stereotypes for Robust Information Prioritization. Workshop on Decentralized, Agent Based and Social Approaches to User Modelling,
    ID article: 2289


  231. I-Hsien Ting, Chris Kimble and Daniel Kudenko. 2005. UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to Improve a Web Site's Design. Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence(WI2005):179--185, Compiegne, France,
    http://www-users.cs.york.ac.uk/%7Ederrick/document/papers/wi2005.pdf ,
    ID article: 2291


  232. M. Bartlett, A. Frisch, Y. Hamadi and I. Miguel. 2005. The temporal knapsack problem and its solution. Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, Ed: R. Barták and M. Milano, Springer Berlin / Heidelberg, Lecture Notes in Computer Science, 3524:811-815,
    http://dx.doi.org/10.1007/11493853_5 ,
    ID article: 3330.

    Abstract:
    This paper introduces a problem called the temporal knapsack problem, presents several algorithms for solving it, and compares their performance. The temporal knapsack problem is a generalisation of the knapsack problem and specialisation of the multidimensional (or multiconstraint) knapsack problem. It arises naturally in applications such as allocating communication bandwidth or CPUs in a multiprocessor to bids for the resources. The algorithms considered use and combine techniques from constraint programming, artificial intelligence and operations research.


  233. B. Martínez-Hernández and A. M. Frisch. 2005. Towards the Systematic Generation of Channelling Constraints. 11th International Conference on Principles and Practice of Constraint Programming (CP 2005), Ed: Peter Van Beek, Springer:859,
    ID article: 2302


  234. Thimal Jayasooriya and Suresh Manandhar. 2005. Lightweight natural language processing. IEE/NWTM Perspectives in Pervasive Computing,
    http://www-users.cs.york.ac.uk/~suresh/papers/LNLP.pdf ,
    ID article: 2874


  235. Spiros Kapetanakis, Daniel Kudenko and Malcolm Strens. 2005. Learning to Coordinate Using Commitment Sequences in Cooperative Multi-Agent Systems. Adaptive Agents and Multi-Agent Systems II, Ed: Daniel Kudenko and Dimitar Kazakov and Eduardo Alonso, Springer LNAI 3394,
    ID article: 2285


  236. Ed: D. Kudenko and D. Kazakov and E. Alonso. 2005. Adaptive Agents and Multi-Agent Systems II, Ed: D. Kudenko and D. Kazakov and E. Alonso, Springer, Lecture Notes in Artificial Intelligence, 3394,
    http://www.springeronline.com/3-540-25260-6 ,
    ID article: 2295


  237. Matthew Grounds and Daniel Kudenko. 2005. Combining Reinforcement Learning with Symbolic Planning. Proceedings of the Fifth European Workshop on Adaptive Agents and Multi-Agent Systems,
    http://www-users.cs.york.ac.uk/%7Emattg/papers/adaptiveagents.pdf ,
    ID article: 2288


  238. Nicos Angelopoulos and James Cussens. 2005. Exploiting Informative Priors for Bayesian Classification and Regression Trees. Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05):641--646, Edinburgh,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/ijcai05.pdf ,
    ID article: 2307.

    Abstract:
    A general method for defining informative priors on statistical models is presented and applied specifically to the space of classification and regression trees. A Bayesian approach to learning such models from data is taken, with the Metropolis-Hastings algorithm being used to approximately sample from the posterior. By only using proposal distributions closely tied to the prior, acceptance probabilities are easily computable via marginal likelihood ratios, whatever the prior used. Our approach is empirically tested by varying (i) the data, (ii) the prior and (iii) the proposal distribution. A comparison with related work is given.


  239. Thimal Jayasooriya and Suresh Manandhar. 2005. Networking in a smart home - providing lightweight networking services to heteregeneous devices . Perspectives in Pervasive Computing.. Proceedings of the NTWM Interface Event, IEE.,
    http://www-users.cs.york.ac.uk/~suresh/papers/NIASH-PLNSTHD.PIPC.pdf ,
    ID article: 2877


  240. James Cussens. 2005. Deductive reasoning and statistical inference. Encyclopedia of Statistics in Behavioral Science, Ed: Brian Everitt and David C. Howell, Wiley,
    ID article: 2305.

    Abstract:
    Connections and contrasts between deductive reasoning and statistical inference are given. Bayesian statistical inference is analysed, including an account of its relation to deductive logic and the status of prior distributions. Classical statistical inference is investigated with a focus on hypothesis testing.


  241. D. Kazakov and M. Bartlett. 2005. Could navigation be the key to language?. Proceedings of the 2nd Symposium on the Emergence and Evolution of Linguistic Communication (EELC'05):50-55,
    http://www.cs.york.ac.uk/aig/papers/Kazakov_2005.pdf ,
    ID article: 3338.

    Abstract:
    This article analyses navigation and language parsing as two instances of the same abstract computation, and suggests that the tool needed may have evolved to serve the former task, and was then reused for the latter. Supporting evidence for the idea, based on the authors’ concept of ‘songline’ navigation, is discussed in the context of current linguistic, psychological and neuroscience research. The discussion is concluded with an outline of a number of experiments that could shed further light on the subject.


  242. Alan M. Frisch, Brahim Hnich, Ian Miguel and Barbara M. Smith. 2005. Transforming and Refining Abstract Constraint Specifications. 6th International Symposium on Abstraction, Reformulation and Approximation (SARA), LNAI 3607:76-91,
    ID article: 2299


  243. Thomas de Simone and Dimitar Kazakov. September 2005. Using WordNet Similarity and Antonymy Relations to Aid Document Retrieval. Recent Advances in Natural Language Processing (RANLP 2005), Borovets, Bulgaria,
    http://www-users.cs.york.ac.uk/%7Ekazakov/papers/desimone-kazakov-crc.pdf ,
    ID article: 2292


  244. Ed: Edward Curry and Enda Ridge. 2005. Proceedings of the First International Semantic Web Doctoral Symposium, Ed: Edward Curry and Enda Ridge, Digital Enterprise Research Institute, Galway, Ireland,
    ID article: 2697


  245. Edward Curry and Enda Ridge. 2005. The Collective: A Common Information Service for Self-Managed Middleware. Proceedings of the Fourth Workshop on Reflective and Adaptive Middleware Systems, ACM Press, ACM International Conference Proceeding Series, 116, Grenoble, France,
    ID article: 2696.

    Abstract:
    As the deployment of self-managed reflective middleware platforms increases, the process of collecting and examining information used within the reflective process becomes ever more complex. The quality of such information is vital to ensure the successful outcome of the self-management process. However, the cost associated with the collection of this information plays a major role in influencing the success of a self-managed system. Within typical deployment environments it is not uncommon for multiple self-managed systems to be deployed, each collecting information for use within their respective reflective computations. In many cases, these systems will collect the same information, replicating the e®ort required to retrieve the information. Such replication could be avoided by sharing information between systems to reduce the overall cost of collection within the deployment environments. Current self-managed systems lack adequate support for information collection and sharing. This work proposes the use of an independent information service to assist in the collection and management of information within self-managed middleware systems.


  246. Ioannis Klapaftis and Suresh Manandhar. 2005. Google & Wordnet based Word Sense Disambiguation. Proceedings of the Workshop on Learning and Extending Ontologies by using Machine Learning methods, held at the 22nd International Conference on Machine Learning (ICML05), Bonn, Germany,
    http://www-users.cs.york.ac.uk/~suresh/papers/G&WBWSD.pdf ,
    ID article: 2873


  247. Thimal Jayasooriya and Suresh Manandhar. 2005. Networking in a smart home - providing lightweight networking services to hetergeneous devices. IEE/NWTM Perspectives in Pervasive Computing,
    http://www-users.cs.york.ac.uk/~suresh/papers/NIASH-PLNSTHD.pdf ,
    ID article: 2875


  248. Spiros Kapetanakis and Daniel Kudenko. 2005. Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-Agent Systems. Adaptive Agents and Multi-Agent Systems II, Ed: Daniel Kudenko and Dimitar Kazakov and Eduardo Alonso, Springer LNAI 3394,
    ID article: 2286


  249. Alan M. Frisch, Chris Jefferson, Bernadette Martínez-Hernández and Ian Miguel. 2005. The Rules of Constraint Modelling. Nineteenth Int. Joint Conf. On Artificial Intelligence (IJCAI):109-116,
    ID article: 2297


  250. M. Bartlett and D. Kazakov. 2005. The origins of syntax: From navigation to language. Connection Science, Taylor & Francis, 17(3):271-288,
    http://dx.doi.org/10.1080/09540090500282479 ,
    ID article: 3337.

    Abstract:
    This article suggests that the parser underlying human syntax may have originally evolved to assist navigation, a claim supported by computational simulations as well as evidence from neuroscience and psychology. We discuss two independent conjectures about the way in which navigation could have supported the emergence of this aspect of the human language faculty: firstly, by promoting the development of a parser; and secondly, by possibly providing a topic of discussion to which this parser could have been applied with minimum effort. The paper summarizes our previously published experiments and provides original results in support of the evolutionary advantages this type of communication can provide, compared with other foraging strategies. Another aspect studied in the experiments is the combination and range of environmental factors that make communication beneficial, focusing on the availability and volatility of resources.We suggest that the parser evolved for navigation might initially have been limited to handling regular languages, and describe a mechanism that may have created selective pressure for a context-free parser.


  251. Caron, Vincent and Andrews, Pierre. 2005. SPIP, mémento, Eyrolles, Paris,
    http://www.eyrolles.com/Informatique/Livre/9782212117325/livre-memento-spip.php ,
    ID article: 2308


  252. D. Kazakov and M. Bartlett. 2004. Cooperative navigation and the faculty of language. Applied Artificial Intelligence, Taylor & Francis, 18(9):885-901,
    http://dx.doi.org/10.1080/08839510490509072 ,
    ID article: 3340.

    Abstract:
    This paper presents an approach to simulating the evolution of language in which communication is viewed as an emerging phenomenon with both genetic and social components. A model is presented in which a population of agents is able to evolve a shared grammatical language from a purely lexical one, with critical elements of the faculty of language developed as a result of the need to navigate in and exchange information about the environment.


  253. Nicos Angelopoulos. 2004. Upsh: A Unix to Prolog Shell. Workshop on Logic Programming Environments. Satellite workshop to ICLP'04, Saint-Malo, France,
    http://www-users.cs.york.ac.uk/%7Enicos/pbs/Wple04.ps.gz ,
    ID article: 2327


  254. Hnich, B., Kiziltan, Z., Miguel, I. and Walsh, T.. 2004. Hybrid Modelling for Robust Solving. Annals of Operations Research, 130(1):19--39,
    ID article: 2319


  255. Wolfgang David Cirilo de Melo and James Cussens. 2004. Leibniz on Estimating the Uncertain: An English Translation of De incerti aestimatione with Commentary. Leibniz Review, 14:31--53,
    ID article: 2322.

    Abstract:
    Leibniz's De incerti aestimatione, which contains his solution to the division problem, has not received much attention, let alone much appreciation. This is surprising because it is in this work that the definition of probability in terms of equally possible cases appears for the first time. The division problem is used to establish and test probability theory; it can be stated as follows: if two players agree to play a game in which one has to win a certain number of rounds in order to win the pool, but if they break the game off before either of them has won the required number of rounds, how should the pool be distributed? Our article has two aims: it provides the readers with the first English translation of De incerti aestimatione, and it also gives them a brief commentary that explains Leibniz's philosophical and mathematical concepts necessary in order to understand this work. The translation is as literal as possible throughout; it shows how Leibniz struggled at times to find a solution to the division problem and how he approached it from different angles. The commentary discusses Leibniz's views on four key concepts: fairness, hope, authority and possibility. The commentary then outlines how Leibniz attempted to solve the problem of division.


  256. Alan M. Frisch, Christopher Jefferson, Bernadette Martínez-Hernández and Ian Miguel. 2004. The Rules of Modelling: Towards Automatic Generation of Constraint Programs.. Proceedings of the 3rd International Workshop on Modelling and Reformulating Constraint Satisfaction: Towards Systematisation and Automation Problems:78--94,
    ID article: 2314


  257. Alonso, Eduardo. 2004. Rights and Argumentation in Open Multi-Agent Systems. Artificial Intelligence Review, 21,
    ID article: 2534


  258. Alan M. Frisch, Brahim Hnich, Ian Miguel and Barbara M. Smith. 2004. Transforming and Refining Abstract Constraint Specifications. Proceedings of the Joint Annual Workshop of ERCIM/CoLogNet on Constraint Solving and Constraint Logic Programming, 2004,
    ID article: 2318


  259. Nicos Angelopoulos and James Cussens. September 2004. Extended Stochastic Logic Programs for Informative Priors over C&RTs. Proceedings of the work-in-progress track of the Fourteenth International Conference on Inductive Logic Programming (ILP04), Ed: Rui Camacho and Ross King and Ashwin Srinivasan:7--11, Porto,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/ilp04_wip.pdf ,
    ID article: 2325.

    Abstract:
    A general method for defining informative priors on statistical models is presented and applied specifically to the space of classification and regression trees. Our aim is towards a Bayesian approach to learning such models from data, with the Metropolis-Hastings algorithm used to sample from the posterior. We present some preliminary results where we empirically tested the methodology.


  260. Lyndon Drake and Alan M. Frisch. 2004. The Interaction Between Inference and Branching Heuristic. Theory and Applications of Satisfiability Testing, Sixth International Conference (Selected Revised Papers),, Springer, LNCS 2919:370--382,
    ID article: 2317


  261. James Cussens. December 2004. Integrating by Separating: Combining Probability and Logic with ICL, PRISM and SLPs, APRIL project report,
    ID article: 2323


  262. D. Kazakov and M. Bartlett. 2004. Social learning through evolution of language. Artificial Evolution, 6th International Conference Evolution Artificielle (EA 2003), Springer, Lecture Notes in Computer Science, 2936:397-408,
    http://dx.doi.org/10.1007/b96080 ,
    ID article: 3342.

    Abstract:
    This paper presents an approach to simulating the evolution of language in which communication is viewed as an emerging phenomenon with both genetic and social components. A model is presented in which a population of agents is able to evolve a shared grammatical language from a purely lexical one, with critical elements of the faculty of language developed as a result of the need to navigate in and exchange information about the environment.


  263. Alan M. Frisch, Christopher Jefferson and Ian Miguel. 2004. Symmetry Breaking as a Prelude to Implied Constraints: A Constraint Modelling Pattern. Proceedings of the 16th European Conference on Artifical Intelligence:171--175,
    ID article: 2312


  264. Yuan, T.. 2004. Human Computer Debate, a Computational Dialectics Approach, Ph.D. Thesis - Computer Science, Leeds Metropolitan University,
    ID article: 3039


  265. J. Sedding and D. Kazakov. 2004. WordNet-based Text Document Clustering. Proceedings of the Third Workshop on Robust Methods in Analysis of Natural Language Data (ROMAND):104--113, Geneva,
    http://www-users.cs.york.ac.uk/%7Ekazakov/papers/SeddingKazakov-paperRomand04.pdf ,
    ID article: 2329


  266. D. Kazakov and M. Sweet. 2004. Evolving the Game of Life. Proceedings of the Fourth Symposium on Adaptive Agents and Multi-Agent Systems (AAMAS-4),
    http://www-users.cs.york.ac.uk/%7Ekazakov/papers/paper-aisb.pdf ,
    ID article: 2332


  267. Alan M. Frisch and Christopher Jefferson. 2004. On the Effectiveness of Set and Multiset Representations in Constraint Programming. Proceedings of the 3rd International Workshop on Modelling and Reformulating Constraint Satisfaction Problems: Towards Systematisation and Automation:125--141,
    ID article: 2315


  268. Yuan, T., Moore, D. and Grierson, A.. 2004. An Assessment of Dialogue Strategies for a Human Computer Debating System, via Computational Agents, In Proceedings of ECAI'2004 Workshop on Computational Models of Natural Argument:17-24, Valencia Spain,
    Yorkcategory: D - refereed international conference paper,
    http://www.csc.liv.ac.uk/~floriana/CMNA4/D.pdf ,
    ID article: 3028


  269. M. Bartlett and D. Kazakov. 2004. The role of environment structure in multi-agent simulations of language evolution. Proceedings of the Fourth Symposium on Adaptive Agents and Multi-Agent Systems (AAMAS-4),
    http://www.cs.york.ac.uk/aig/papers/Bartlett_2004.pdf ,
    ID article: 3344.

    Abstract:
    This paper presents a multi-agent system which has been developed in order to test our theories of language evolution. We propose that language evolution is an emergent behaviour, which is influenced by both genetic and social factors and show that a multi-agent approach is thus most suited to practical study of the salient issues. We present a hypothesis that the original function of language in humans was to share navigational information, and show experimental support for this hypothesis through results comparing the performance of agents in a series of environments. In particular, we study how the degree to which language use is beneficial varies with a particular property of the environment structure, that of the distance between resources needed for survival.


  270. Tarim, S.A. and Miguel, I.. 2004. Echelon Stock Formulation of Arborescent Distribution Systems: An Application to the Wagner-Whitin Problem. Proceedings of the International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimisation Problems (CPAIOR):302--318,
    ID article: 2320


  271. Nicos Angelopoulos. 2004. Probabilistic space partitioning in Constraint Logic Programming. Ninth Asian Computing Science Conference, Chiang Mai, Thailand,
    http://www-users.cs.york.ac.uk/%7Enicos/pbs/Asian04.ps.gz ,
    ID article: 2326


  272. M. Bartlett, A. Frisch, Y. Hamadi and I. Miguel. 2004. Efficient algorithms for selecting advanced reservations(2004), Microsoft Research,
    http://research.microsoft.com/pubs/67307/tr-2004-132.pdf ,
    ID article: 3331.

    Abstract:
    Grid computing leverages and generalizes distributed computing by focusing on large scale resource sharing for high performance and innovative applications. Those applications require the simultaneous or successive use of various grid resources. An important problem that faces Grid computing is then to ensure the timed access to various resources. One possible way to achieve this is to negotiate some service level agreement between the application and the infrastructure. This is called Advanced Reservations (AR). This paper focuses on this important problem. We take the rationale of a Grid resource broker which maximizes its utility with respect to incoming demands for resource access. We define two new algorithms for this. The first one computes optimal solutions through a problem decomposition strategy while the second one uses greedy exploration to quickly find a solution.


  273. Alan M. Frisch, Christopher Jefferson, Bernadette Martínez-Hernández and Ian Miguel. 2004. Generating Effective Constraint Programs: An Application of Automated Reasoning. Proceedings of the 11th Workshop on Automated Reasoning: Bridging the Gap Between Theory and Practice,
    ID article: 2313


  274. I-Hsien Ting, Chris Kimble and Daniel Kudenko. 2004. Visualizing and Classifying the Pattern of User's Browsing Behaviour for Website Design Recommendation. First International Workshop on Knowledge Discovery in Data Stream (ECML/ PKDD 2004):101-102, Pisa, Italy,
    http://www-users.cs.york.ac.uk/%7Ederrick/document/papers/ecml2004.pdf ,
    ID article: 2334


  275. Marco De Boni and Suresh Manandhar. 2004. Implementing Clarification Dialogue in Open-Domain Question Answering. Journal of Natural Language Engineering,
    http://www-users.cs.york.ac.uk/~suresh/papers/ICDODQ.pdf ,
    ID article: 3201


  276. Yuan, T.. 2004. Debate with Computers, a Computational Dialectics Approach, Poster in Leeds Metropolitan University Annual Research Students Conference,
    Yorkcategory: E - other reports, unrefereed papers, yellow report etc.,
    ID article: 3041


  277. Ed: Suresh Manandhar and Jim Austin and U.B. Desai and Yoshio Oyanagi and Asoke Talukder. 2004. 2nd Asian Applied Computing Conference, Ed: Suresh Manandhar and Jim Austin and U.B. Desai and Yoshio Oyanagi and Asoke Talukder, Springer-Verlag, Lecture Notes in Computer Science, Volume 3285,
    ID article: 2336


  278. D. Kazakov. 2004. Evolutionary Algorithms with Extended Fitness, Department of Computer Science, University of York, UK,
    http://www-users.cs.york.ac.uk/%7Ekazakov/papers/tech-rep.pdf ,
    ID article: 2333


  279. Alan M. Frisch, Youssef Hamadi and Ian Miguel. June 2004. An Overview of the Gridline Project. Workshop on Planning and Scheduling for Web and Grid Services,,
    ID article: 2316


  280. Suresh Manandhar, Jim Austin, U.B. Dessai and Yoshio Oyanagi. 2004. 2nd Asian Applied Computing Conference. Lecture Notes in Computer Science. Volume 3285, Springer-Verlag,
    http://www-users.cs.york.ac.uk/~suresh/papers/2AACC.pdf ,
    ID article: 2880


  281. Nicos Angelopoulos and James Cussens. 2004. On the implementation of MCMC proposals over Stochastic Logic Programs. Colloquium on Implementation of Constraint and LOgic Programming Systems. Satellite workshop to ICLP'04, Saint-Malo, France,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/ciclops04.ps.gz ,
    ID article: 2324


  282. Thimal Jayasooriya and Suresh Manandhar. 2004. Using Document Dimensions for Enhanced Information Retrieval. Proceedings of 2nd Asian Applied Computing Conference, Springer-Verlag, LNCS Vol 3285,
    http://www.springerlink.com/index/948cqhth01j848ab.pdf ,
    ID article: 3151


  283. Z. Lock and D. Kudenko. 2003. Multi-Component User Models of Team Members. UM`03 Workshop on User and Group Models for Web-based Adaptive Collaborative Environments,
    ID article: 2452


  284. S. Kapetanakis, D. Kudenko and M. Strens. 2003. Reinforcement Learning Approaches to Coordination in Cooperative Multi-Agent Systems. Adaptive Agents and Multi-Agent Systems, Ed: E. Alonso and D. Kudenko and D. Kazakov, Springer LNAI 2636,
    ID article: 2451


  285. Ed: James Cussens and Alan M. Frisch. August 2003. Journal of Machine Learning Research: Special Issue on Inductive Logic Programming, Ed: James Cussens and Alan M. Frisch, MIT Press, 4:413-521,
    http://www.www.ai.mit.edu/projects/jmlr/ ,
    ID article: 2375


  286. M. De Boni M. and M. Prigmore. 2003. Privacy and the Information Economy. Proceedings of the IADIS Internations e-Society,
    ID article: 2339


  287. M. De Boni, J.L. and S. Manandhar. 2003. The YorkQA prototype question answering system. Proceedings of the 11th Text Retrieval Conference (TREC),
    http://www-users.cs.york.ac.uk/~suresh/papers/YorkQAPrototype.pdf ,
    ID article: 3202


  288. Frisch, A.M., Miguel, I., Kiziltan, Z. and Hnich, B.. 2003. Multiset Ordering Constraints. Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, Ed: Gottlob, G.:318--332,
    ID article: 2370


  289. Flener, P., Frisch, A.M., Hnich, B. and Jefferson, C.. 2003. Breaking Symmetries in Matrix Models: A Brief Overview. Proceedings of the Tenth Workshop on Automated Reasoning:27--28,
    ID article: 2372


  290. P. Praveen, M. Tambe, S. Kapetanakis and S. Kraus. 2003. Between collaboration and competition: An initial formalization using Distributed POMDPs. Proceedings of the Fifth Workshop on Game Theoretic and Decision Theoretic Agents (GTDT03), part of the Adaptive Agents and Multi-Agent Systems conference (AAMAS-03), Melbourne, Australia,
    ID article: 2455


  291. D. Kazakov and S. Dobnik. 2003. Inductive Learning of Lexical Semantics with Typed Unification Grammars. Grammars. Oxford Working Papers in Linguistics, Philology, and Phonetics, Oxford University,
    http://www-users.cs.york.ac.uk/%7Ekazakov/papers/oxford-paper.ps ,
    ID article: 2394


  292. Yuan, T., Moore, D. and Grierson, A.. 2003. A Conversational Agents System as a Test-Bed to Study Philosophical Model “DC”, In Proceedings of IJCAI’2003 Workshop on Computational Models of Natural Argument:45-50, Acapulco, Mexico,
    Yorkcategory: D - refereed international conference paper,
    http://www.computing.dundee.ac.uk/staff/creed/research/cmna2005/02yuan.pdf ,
    ID article: 3031


  293. Miguel, I. and Shen, Q.. 2003. Fuzzy rrDFCSP and Planning. Artificial Intelligence, 148(1):11-52,
    ID article: 2408


  294. Jefferson, C., Miguel, A., Miguel, I. and Tarim, A.. 2003. Modelling and Solving English Peg Solitaire. Proceedings of the Fifth International Workshop on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR):261--275,
    ID article: 2409


  295. M. De Boni and S. Manandhar. 2003. The use of sentence similarity as a semantic relevance metric for QA. Proceedings of the AAAI Symposium on New Directions in Question Answering,
    http://www.aaai.org/Papers/Symposia/Spring/2003/SS-03-07/SS03-07-024.pdf ,
    ID article: 3152


  296. D. Kudenko, M. Bauer and D. Dengler. 2003. Group Decision Making Through Mediated Discussions. Proceedings of the Ninth International Conference on User Modeling (UM `03), Springer LNAI 2702,
    ID article: 2449


  297. S. Kapetanakis, D. Kudenko and M. Strens. 2003. Learning to Coordinate Using Commitment Sequences in Cooperative Multi-Agent Systems. Proceedings of the Third Symposium on Adaptive Agents and Multi-Agent Systems,
    ID article: 2453


  298. Alan M. Frisch and Warwick Harvey. September 2003. Constraints for Breaking All Row and Column Symmetries in a Three-by-Two Matrix. Proceedings of the Third International Workshop on Symmetry in Constraint Satisfaction Problems,,
    ID article: 2373


  299. M. De Boni and M. Prigmore. 2003. Growing in Cyberspace: Children's Rights Online. Proceedings of UKAIS,
    http://www-users.cs.york.ac.uk/~mdeboni/papers/childrens_privacy.pdf ,
    ID article: 2340


  300. Frisch, A.M., Miguel, I. and Walsh, T.. 2003. Refining Abstract Specifications of Constraint Satisfaction Problems. Proceedings of the Tenth Workshop on Automated Reasoning:29--31,
    ID article: 2374


  301. Suresh Manandhar, Armagan Tarim and Toby Walsh. 2003. Scenario-based Stochastic Constraint Programming, Proceedings of IJCAI, pages 257--262, Acapulco, Mexico.,
    http://www-users.cs.york.ac.uk/~suresh/papers/SSCP.pdf ,
    ID article: 3045


  302. Bakewell, A., Frisch, A.M. and Miguel, I.. 2003. Towards Automatic Modelling of Constraint Satisfaction Problems: A System Based on Compositional Refinement. Proceedings of the Second International Workshop on Modelling and Reformulating Constraint Satisfaction Problems, Ed: Frisch, A.M.:2--17,
    ID article: 2371


  303. Joanna Moy and Suresh Manandhar. 2003. Modelling the Emergence of Case. Proceedings of Language Evolution and Computation Workshop/Course at ESSLLI:42-51,
    http://www.isrl.uiuc.edu/~amag/langev/paper/moy03caseEmergence.html ,
    ID article: 2959


  304. Heather Turner and Dimitar Kazakov. 2003. Stochastic Simulation of Inherited Kinship-Driven Altruism. Adaptive Agents and Multiagent Systems, Lecture Notes in Computer Science 2636, Springer-Verlag,
    ID article: 2393


  305. S. Kapetanakis, D. Kudenko and M. Strens. 2003. Learning of Coordination in Cooperative Multi-Agent Systems using Commitment Sequences. Artificial Intelligence and the Simulation of Behavior, 1(5),
    ID article: 2448


  306. De Boni, Marco and Manandhar, Suresh. 2003. An analysis of clarification dialogue for question answering. Proceedings of HLT-NAACL, Edmonton, Canada,
    http://www-users.cs.york.ac.uk/~suresh/papers/CDQA_NAACL.pdf ,
    ID article: 3203


  307. Ed: E. Alonso and D. Kudenko and D. Kazakov. 2003. Adaptive Agents and Multi-Agent Systems, Ed: E. Alonso and D. Kudenko and D. Kazakov, Springer LNAI 2636,
    ID article: 2454


  308. Frisch, A.M., Jefferson, C. and Miguel, I.. 2003. Constraints for Breaking More Row and Column Symmetries. Proceedings of the Ninth International Conference on Principles and Practice of Constraint Programming LNCS 2833, Ed: Rossi, F.:318--332,
    ID article: 2369


  309. S. Baldes, M. Bauer, D. Dengler and A. Jameson. 2003. MIAU -- Supporting Group Decisions in E-Commerce Applications. Proceedings of the Tenth International Conference on Human-Computer Interaction (HCII `03),
    ID article: 2450


  310. Yuan, T., Moore, D. and Grierson, A.. 2003. Computational Agents as a Test-Bed to Study Philosophical Model “DE”, A Development of Mackenzie’s “DC”. Journal of Informal Logic, 23(3):263-284,
    Yorkcategory: C - refereed journal paper,
    http://web2.uwindsor.ca/faculty/arts/philosophy/IL/Past/tc23-3.htm ,
    ID article: 3015


  311. James Cussens. August 2003. Individuals, relations and structures in probabilistic models. IJCAI Workshop on Learning Statistical Models from Relational Data (SRL2003), Ed: Lise Getoor and David Jensen:32--36, Acapulco, Mexico,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/srl03.pdf ,
    ID article: 2446.

    Abstract:
    Relational data is equivalent to non-relational structured data. It is this equivalence which permits probabilistic models of relational data. Learning of probabilistic models for relational data is possible because one item of structured data is generally equivalent to many related data items. Succession and inclusion are two relations that have been well explored in the statistical literature. A description of the relevant statistical approaches is given. The representation of relational data via Bayesian nets is examined, and compared with PRMs. The paper ends with some cursory remarks on structured objects.


  312. Yuan, T., Moore, D. and Grierson, A.. 2003. A Preliminary Evaluation of the Usability of a Human Computer Debate Dialogue Model, HCI 2003, Design for Society:21-24, Bath, UK,
    Yorkcategory: D - refereed international conference paper,
    http://www-users.cs.york.ac.uk/~tommy/Papers/HCI2003.pdf ,
    ID article: 3030


  313. Santos Costa, Vítor, David Page, Maleeha Qazi and James Cussens. 2003. CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--2003), Ed: Uffe Kjae rulff and Christopher Meek, Morgan Kaufmann:517--524, Acapulco, Mexico,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/uai03.ps.gz ,
    ID article: 2447.

    Abstract:
    In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially-quantified variables, are represented by terms built from Skolem functors. In an analogy to probabilistic relational models (PRMs), we wish to represent the joint probability distribution over missing values in a database or logic program using a Bayesian network. This paper presents an extension of logic programs that makes it possible to specify a joint probability distribution over terms built from Skolem functors in the program. Our extension is based on constraint logic programming (CLP), so we call the extended language CLP(BN). We show that CLP(BN) subsumes PRMs; this greater expressivity carries both advantages and disadvantages for CLP(BN). We also show that algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP(BN) programs. An implementation of CLP(BN) is publicly available as part of YAP Prolog at http://www.cos.ufrj.br/~vitor/Yap/clpbn.


  314. Enrique Alfonseca and Suresh Manandhar. 2002. An Unsupervised Method for Generalised Named Entity Recognition and Automated Concept Discovery. 1st International Wordnet conference, Mysore, India,
    http://www-users.cs.york.ac.uk/~suresh/papers/AUMFGNERAACD.pdf ,
    ID article: 2890


  315. Frisch, A.M., Miguel, I. and Walsh, T.. 2002. CGRASS: A System for Transforming Constraint Satisfaction Problems. Proceedings of the Joint Workshop of ther ERCIM Working Group on Constraints and the CologNet area on Constraint and Logic Programming on Constraint Solving and Constraint Logic Programming, Ed: Apt, K. R. and Fages, F. and Freuder, E.C. and O'Sullivan, B. and Rossi, F. and Walsh, T.:23--36,
    http://www.cs.york.ac.uk/aig/projects/implied/docs/ERCIM02.ps.gz ,
    ID article: 2407


  316. Alfonseca, Enrique and Manandhar, Suresh. 2002. Distinguishing Concepts and Instances in WordNet. First International Conference on General WordNet, Mysore, India,
    http://www-users.cs.york.ac.uk/~suresh/papers/DCAIIW.pdf ,
    ID article: 2891


  317. Guido Minnen. 2002. Reviewed by Suresh Manandhar, Efficient Processing with Constraint-Logic Grammar Using Grammar Compilation. Journal Of Computational Linguistics, Stanford: CSLI Publications,
    http://www-users.cs.york.ac.uk/~suresh/papers/Reviewed by Suresh Manandhar, Efficient Processing with Constraint-Logic Grammar Using Grammar Compilation.pdf ,
    ID article: 2757


  318. Yuan, T.. 2002. Evaluation of Philosophical Models in a Computational Environment, In Proceedings of the First Annual Faculty Research Student Conference (FRSC’02), Leeds Metropolitan University,
    Yorkcategory: E - other reports, unrefereed papers, yellow report etc.,
    ID article: 3036


  319. Alfonseca, Enrique and Manandhar, Suresh. 2002. Proposal for Evaluating Ontology Refinement Methods. Language Resources and Evaluation (LREC-2002), Las Palmas, Spain,
    http://www-users.cs.york.ac.uk/~suresh/papers/PFEORM.pdf ,
    ID article: 2888


  320. Frisch, A.M., Hnich, B., Miguel, I. and Smith, B.M.. 2002. Towards Model Reformulation at Multiple Levels of Abstraction. Proceedings of the International Workshop on Reformulating Constraint Satisfaction Problems, Ed: Frisch, A.M.:42--56,
    http://4c.ucc.ie/~tw/fhmswreform02.pdf ,
    ID article: 2405


  321. Nicos Angelopoulos and James Cussens. 2002. Prolog issues of an MCMC algorithm. Web-Knowledge Management and Decision Support - Selected Papers from the 14th International Conference on Applications of Prolog, Ed: U. Geske and O. Bartenstein and M. Hannebauer and O. Yoshie, Springer, LNAI, 2543:191--202, Berlin,
    ID article: 2444


  322. Enrique Alfonseca and Suresh Manandhar. 2002. Extending a Lexical Ontology by a Combination of Distributional Semantics Signatures. In EKAW-2002, Springer-Verlag, Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web, Lecture Notes of Artificial Intelligence, Subseries of Lecture Notes , Siguenza, Spain,
    http://www-users.cs.york.ac.uk/~suresh/papers/EALOBACODSS.pdf ,
    ID article: 2893


  323. James Cussens. 2002. Issues in Learning Language in Logic. Computational Logic: Logic Programming and Beyond, Ed: Antonis C. Kakas and Fariba Sadri, Springer, LNAI, 2408:491--505, Berlin,
    http://link.springer.de/link/service/series/0558/bibs/2408/24080491.htm ,
    ID article: 2442.

    Abstract:
    Selected issues concerning the use of logical representations in machine learning of natural language are discussed. It is argued that the flexibility and expressivity of logical representations are particularly useful in more complex natural language learning tasks. A number of inductive logic programming (ILP) techniques for natural language are analysed including the CHILL system, abduction and the incorporation of linguistic knowledge, including active learning. Hybrid approaches integrating ILP with manual development environments and probabilistic techniques are advocatd.


  324. Alfonseca, Enrique and Manandhar, Suresh. 2002. A Framework for Constructing Temporal Models from Texts. FLAIRS-2002, Pensacola, Florida,
    http://www.aaai.org/Papers/FLAIRS/2002/FLAIRS02-089.pdf ,
    ID article: 3157


  325. E. Alfonseca, M. De Boni, J.L. Jara and S. Manandhar. 2002. A prototype Question Answering system using syntactic and semantic information for answer retrieval. Proceedings of the 10th Text Retrieval Conference (TREC-10),
    ID article: 3155


  326. Flener, P., Frisch, A.M., Hnich, B. and Kiziltan, Z.. 2002. Breaking Row and Column Symmetries in Matrix Models. Proceedings of the Eighth International Conference on Principles and Practice of Constraint Programming, Ed: van Hentenryck, P.:462--476,
    http://4c.ucc.ie/~tw/ffhkmpwcp2002.pdf ,
    ID article: 2402


  327. Page, David, Zhan, Fenghuang, Cussens, James and Waddell, Michael. 2002. Comparative Data Mining for Microarrays: A Case Study Based on Multiple Myeloma(1453), Computer Sciences Department, University of Wisconsin,
    ftp://ftp.cs.wisc.edu/pub/tech-reports/reports/2002/tr1453.ps.Z ,
    ID article: 2443.

    Abstract:
    Supervised machine learning and data mining tools have become popular for the analysis of gene expression microarray data. They have the potential to uncover new therapeutic targets for diseases, to predict how patients will respond to specific treatments, and to uncover regulatory relationships among genes in normal and disease situations. Comparative experiments are needed to identify the advantages of the leading supervised learning algorithms for microarray data, as well as to give direction in methodological decisions. This paper compares support vector machines, Bayesian networks, decision trees, boosted decision trees, and voting (ensembles of decision stumps) on a new microarray data set for cancer with over 100 samples. The paper provides evidence for several important lessons for mining microarray data, including: (1) Bayes nets and ensembles perform at least as well as other approaches but arguably provide more direct insight; (2) the common practice of throwing out low or negative average differences, or those accompanied by an absent call, is a mistake; (3) looking for consistent differences in expression may be more important than large differences.


  328. Yuan, T., Moore, D. and Grierson, A.. 2002. Educational Human-Computer Debate, A Computational Dialectics Approach, In Proceedings of ECAI'2002 Workshop on Computational Models of Natural Argument:19-22, Lyon, France,
    Yorkcategory: D - refereed international conference paper,
    http://www.csc.liv.ac.uk/~floriana/CMNA/YuanMooreGrierson.pdf ,
    ID article: 3033


  329. Alfonseca, Enrique and Manandhar, Suresh. 2002. Improving an Ontology Refinement Method with Hyponymy Patterns. Language Resources and Evaluation (LREC-2002), Las Palmas, Spain,
    http://www-users.cs.york.ac.uk/~suresh/papers/IAORMWHP.pdf ,
    ID article: 2889


  330. Frisch, A.M., Miguel, I. and Walsh, T.. 2002. Automatically Transforming Constraint Satisfaction Problems: Further Progress. Proceedings of the 9th Workshop on Automated Reasoning, Ed: Walsh, T.,
    http://www.cs.york.ac.uk/aig/projects/implied/docs/ARW02.ps.gz ,
    ID article: 2406


  331. D. Kazakov and M. Bartlett. 2002. A multi-agent simulation of the evolution of language. Proceedings A of the Fifth Multi-Conference of the Information Society (IS 2002):39-41,
    http://www.cs.york.ac.uk/aig/papers/Kazakov_2002.pdf ,
    ID article: 3343.

    Abstract:
    This paper discusses the evolution of language as an emerging phenomenon with both genetic and social components that are shaped under evolutionary pressure. Communication between relatives is seen as an act of kinship-driven altruism and the chances of survival of such behavour discussed from a Neo-Darwinist point of view. The paper provides motivation for the use of multi-agent systems in the simulation of the evolution of language and describes one setup taking into account the above-mentioned issues.


  332. M. De Boni and S. Manandhar. 2002. Automated discovery of telic relations for WordNet. Proceedings of the first International WordNet conference,
    http://www-users.cs.york.ac.uk/~suresh/papers/ADTRWN.pdf ,
    ID article: 3204


  333. M. De Boni and M. Prigmore. 2002. Cultural aspects of internet privacy. Proceedings of the UKAIS 2002 Conference, Leeds,
    http://www-users.cs.york.ac.uk/~mdeboni/papers/Cultural_Aspects_of_Internet_Privacy.pdf ,
    ID article: 2343


  334. Frisch, A.M., Hnich, B., Kiziltan, Z. and Miguel, I.. 2002. Global Constraints for Lexicographic Orderings. Proceedings of the Eighth International Conference on Principles and Practice of Constraint Programming, Ed: van Hentenryck, P.:93--108,
    http://4c.ucc.ie/~tw/fhkmwcp2002.pdf ,
    ID article: 2403


  335. Flener, P., Frisch, A.M., Hnich, B. and Kiziltan, Z.. 2002. Matrix Modelling: Exploiting Common Patterns in Constraint Programming. Proceedings of the International Workshop on Reformulating Constraint Satisfaction Problems, Ed: Frisch, A.M.:27--41,
    http://4c.ucc.ie/~tw/ffhkmwreform02.pdf ,
    ID article: 2404


  336. James Cussens. 2002. Leibniz and Boole on logic and probability, Unpublished and unsubmitted, so far!,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/mi.ps.gz ,
    ID article: 2321.

    Abstract:
    Combined logical-probabilistic frameworks are very old; often the result of grand attempts to formulate a calculus of general reasoning. A careful account of the assumptions and achievements of these systems can help us address issues in contemporary logical-probabilistic frameworks. This paper aims to provide such an account drawing on the pioneering work of Leibniz and Boole. In Leibniz, we find the first account of epistemic probability in terms of possible worlds. In Boole, we see the extent to which a combined logical-probabilistic calculus is possible using a propositional representation.


  337. Dimitar Kazakov and Daniel Kudenko. 2001. Machine Learning and Inductive Logic Programming for Multi-Agent Systems. Milti-Agent Systems and Applications, Ed: Michael Luck and Vladimír Marík and Olga Stepánková, Springer, LNAI 2086:246--270,
    Yorkcategory: B - part of book (chapter),
    http://www-users.cs.york.ac.uk/~kazakov/papers/acai01.htm ,
    ID article: 2390


  338. Frisch, A.M., Miguel, I. and Walsh, T.. 2001. Modelling a Steel Mill Slab Design Problem. Proceedings of the IJCAI-01 Workshop on Modelling and Solving Problems with Constraints:39--45,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2365


  339. Yuan, T.. 2001. Does Java Provide a Suitable Platform for a Distributed Human Resource System?, M.Sc. Thesis – Software Development, Leeds Metropolitan University,
    ID article: 3038


  340. Colton, S. and Miguel, I.. 2001. Constraint Generation via Automated Theory Formation. Proceedings of the Seventh International Conference on Principles and Practice of Constraint Programming, Ed: Walsh, T.:575--579,
    Yorkcategory: D - Refereed International Conference Paper,
    ID article: 2460


  341. James Cussens. January 2001. Statistical Aspects of Stochastic Logic Programs. Artificial Intelligence and Statistics 2001: Proceedings of the Eighth International Workshop, Ed: Tommi Jaakkola and Thomas Richardson, Morgan Kaufmann:181-186, Key West, Florida,
    Yorkcategory: D - Refereed International Conference Paper,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/jcaistats.ps.gz ,
    ID article: 2441.

    Abstract:
    Stochastic logic programs (SLPs) and the various distributions they define are presented with a stress on their characterisation in terms of Markov chains. Sampling, parameter estimation and structure learning for SLPs are discussed. The application of SLPs to Bayesian learning, computational linguistics and computational biology are considered. Lafferty's Gibbs-Markov models are compared and contrasted with SLPs.


  342. Ed: Toby Walsh. 2001. Proceedings of the Seventh International Conference on Principles and Practice of Constraint Programming, Ed: Toby Walsh, Springer-Verlag, Lecture Notes in Computer Science, 2239,
    Yorkcategory: A - Book,
    ID article: 2490


  343. M. De Boni and M. Prigmore. 2001. A Hegelian basis for information privacy as an economic right. Proceedings of the UKAIS conference,
    http://www.users.cs.york.ac.uk/~mdeboni/papers/Hegelian_Basis_For_E-privacy.pdf ,
    ID article: 2348


  344. Alan M. Frisch and Yuan Zhan. 2001. Restart Strategies for Constraint Satisfaction Problems. Proceedings of the Eighth Workshop on Automated Reasoning: Bridging the Gap between Theory and Practice, Ed: Andrei Voronkov:19--20,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2368


  345. Miguel, I., Jarvis, P. and Shen, Q.. 2001. Efficient Flexible Planning via Dynamic Flexible Constraint Satisfaction. Engineering Applications of Artificial Intelligence, 14(3):301--327,
    Yorkcategory: C - Refereed Journal Paper,
    ID article: 2399


  346. S.H. Muggleton, C.H. Bryant, A. Srinivasan and A. Whittaker. October 2001. Are grammatical representations useful for learning from biological sequence data? - a case study. Journal of Computational Biology, Copyright Mary Ann Liebert, 8(5):493-522,
    http://www.liebertpub.com/ ,
    ID article: 2640


  347. S. Muggleton. 2001. Stochastic Logic Programs. Journal of Logic Programming, Accepted subject to revision,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/slp.ps.gz ,
    ID article: 2645.

    Abstract:
    One way to represent a machine learning algorithm's bias over the hypothesis and instance space is as a pair of probability distributions. This approach has been taken both within Bayesian learning schemes and the framework of U-learnability. However, it is not obvious how an Inductive Logic Programming (ILP) system should best be provided with a probability distribution. This paper extends the results of a previous paper by the author which introduced stochastic logic programs as a means of providing a structured definition of such a probability distribution. Stochastic logic programs are a generalisation of stochastic grammars. A stochastic logic program consists of a set of labelled clauses p:C where p is from the interval [0,1] and C is a range-restricted definite clause. A stochastic logic program P has a distributional semantics, that is one which assigns a probability distribution to the atoms of each predicate in the Herbrand base of the clauses in P. These probabilities are assigned to atoms according to an SLD-resolution strategy which employs a stochastic selection rule. It is shown that the probabilities can be computed directly for fail-free logic programs and by normalisation for arbitrary logic programs. The stochastic proof strategy can be used to provide three distinct functions: 1) a method of sampling from the Herbrand base which can be used to provide selected targets or example sets for ILP experiments, 2) a measure of the information content of examples or hypotheses; this can be used to guide the search in an ILP system and 3) a simple method for conditioning a given stochastic logic program on samples of data. Functions 1) and 3) are used to measure the generality of hypotheses in the ILP system Progol4.2. This supports an implementation of a Bayesian technique for learning from positive ex


  348. Nicos Angelopoulos and James Cussens. August 2001. Markov Chain Monte Carlo using Tree-Based Priors on Model Structure. Proceedings of the Seventeenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--2001), Ed: Jack Breese and Daphne Koller, Morgan Kaufmann, Seattle,
    Yorkcategory: D - Refereed International Conference Paper,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/uai01.ps.gz ,
    ID article: 2554.

    Abstract:
    We present a general framework for defining priors on model structure and sampling from the posterior using the Metropolis-Hastings algorithm. The key ideas are that structure priors are defined via a probability tree and that the proposal distribution for the Metropolis-Hastings algorithm is defined using the prior, thereby defining a cheaply computable acceptance probability. We have applied this approach to Bayesian net structure learning using a number of priors and proposal distributions. Our results show that these must be chosen appropriately for this approach to be successful.


  349. Stephen Pulman and James Cussens. 2001. Grammar learning using Inductive Logic Programming. Oxford University Working Papers in Linguistics, Philology and Phonetics, 6:31--45,
    http://www.clp.ox.ac.uk/people/staff/pulman/pdfpapers/ox_working_papers2.pdf ,
    ID article: 2438.

    Abstract:
    This paper gives a brief introduction to a particular machine learning method known as inductive logic programming. It is argued that this method, unlike many current statistically based machine learning methods, implies a view of grammar learning that bears close affinity to the views linguists have of the logical problem of language acquisition. Two experiments in grammar learning using this technique are described, using a unification grammar formalism, and positive-only data.


  350. Stephen Watkinson and Suresh Manandhar. 2001. A Psychologically Plausible and Computationally Effective Approach to Learning Syntax. Proceedings of the Workshop Computational Natural Language Learning (CoNLL-2001), Ed: Walter Daelemans and R'emi Zajac:160 - 167,
    Yorkcategory: D - Refereed International Workshop Paper,
    http://www-users.cs.york.ac.uk/~suresh/papers/APPACEATLS.pdf ,
    ID article: 2897


  351. J. Slaney and T. Walsh. 2001. Backbones in Optimization and Approximation. Proceedings of 17th IJCAI, IJCAI,
    Yorkcategory: D - Refereed International Conference Paper,
    ID article: 2491


  352. Dimitar Kazakov and Suresh Manandhar. 2001. Unsupervised learning of word segmentation rules with genetic algorithms and inductive logic programming. Machine Learning, 43:121 - 162,
    Yorkcategory: C - Refereed Journal Paper,
    http://www.springerlink.com/index/J7Q24GG20047K870.pdf ,
    ID article: 3158


  353. Ljupco Todorovski, Irene Weber, Nada Lavrac and Olga Stepánková. 2001. Relational Data Mining, Springer:375--388, Berlin,
    Yorkcategory: B - part of book (chapter),
    http://www-ai.ijs.si/SasoDzeroski/RDMBook/ ,
    ID article: 2391


  354. M. Turcotte, S.H. Muggleton and M.J.E. Sternberg. 2001. The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures. Machine Learning, 1:81--96,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ml2000.ps.gz ,
    ID article: 2639.

    Abstract:
    As a form of Machine Learning the study of Inductive Logic Programming (ILP) is motivated by a central belief: relational description languages are better (in terms of accuracy and understandability) than propositional ones for certain real-world applications. This claim is investigated here for a particular application in structural molecular biology, that of constructing readable descriptions of the major protein folds. To the authors' knowledge Machine Learning has not previously been applied systematically to this task. In this application domain the domain expert (thord author) identified a natural divide between essentially propositional features and more structurally-orientated relational ones. The following null hypotheses are tested: 1) for a given ILP system (Progol) provision of relational background knowledge does not increase predictive accuracy, 2) a good propositional learning system (C5.0) without relational background knowledge will outperform Progol with relational background knowledge, 3) relational background knowledge does not produce improved explanatory insight. Null hypotheses 1) and 2) are both refuted on cross-validation results carried out over 20 of the most populated protein folds. Hypothesis 3 is refuted by demonstration of various insightful rules discovered only in the relationally-oriented learned rules.


  355. L.-V. Ciortuz. October 2001. On compilation of head-corner bottom-up chart-based parsing with unification grammars. Proceedings of the IWPT 2001 International Workshop on Parsing Technologies, Beijing, China,
    Yorkcategory: D - Refereed International Conference paper,
    ID article: 2458


  356. James Cussens. 2001. Parameter estimation in stochastic logic programs. Machine Learning, 44(3):245--271,
    Yorkcategory: C - Refereed Journal Paper,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/jcslpmlj.ps.gz ,
    ID article: 2439.

    Abstract:
    Stochastic logic programs (SLPs) are logic programs with labelled clauses which define a log-linear distribution over refutations of goals. The log-linear distribution provides, by marginalisation, a distribution over variable bindings, allowing SLPs to compactly represent quite complex distributions. We analyse the fundamental statistical properties of SLPs addressing issues concerning infinite derivations, `unnormalised' SLPs and impure SLPs. After detailing existing approaches to parameter estimation for log-linear models and their application to SLPs, we present a new algorithm called failure-adjusted maximisation (FAM). FAM is an instance of the EM algorithm that applies specifically to normalised SLPs and provides a closed-form for computing parameter updates within an iterative maximisation approach. We empirically show that FAM works on some small examples and discuss methods for applying it to bigger problems.


  357. T. Walsh. 2001. Stochastic Constraint Programming. Proceedings of the CP'01 Workshop on Modelling and Problem Formulation, Available as APES report from http://www.dcs.st-and.ac.uk/~apes/apesreports.html,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2494


  358. M. De Boni. 2001. A study into communication management problems in a Korean internet company. Management Case Quarterly, Vol 4, n. 4,
    ID article: 2346


  359. Frisch, A.M., Miguel, I. and Walsh, T.. 2001. Extensions to Proof Planning for Generating Implied Constraints. Proceedings of the Ninth Symposium on the Integration of Symbolic Computation and Mechanized Reasoning (Calculemus 01), Ed: Linton, S. and Sebastiani, R.:130--141,
    Yorkcategory: D - Refereed International Conference Paper,
    ID article: 2361


  360. Frisch, A.M., Miguel, I. and Walsh, T.. 2001. Generating Implied Constraints via Proof Planning. Proceedings of the IJCAR-01 Workshop on Future Directions in Automated Reasoning:48--54,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2366


  361. Flener, P., Frisch, A.M., Hnich, B. and Kiziltan, Z.. 2001. Matrix Modelling. Proceedings of the CP'01 Workshop on Modelling and Problem Formulation:1--7,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2362


  362. Colton, S., Drake, L., Frisch, A.M. and Miguel, I.. 2001. Automatic Generation of Implied Constraints: Initial Progress. Proceedings of the 8th Workshop on Automated Reasoning:17--18,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2461


  363. Alonso, Eduardo and Kudenko, Daniel. 2001. Sistemas Lógicos de Múltiples Agentes: Arquitectura e Implementación en Simuladores de Conflictos. Inteligencia Artificial, Special Issue on Development of Multi-Agent Systems, 13:85--93,
    ID article: 2532


  364. Miguel, I. and Shen, Q.. 2001. Solution Techniques for Constraint Satisfaction Problems: Foundations. Artificial Intelligence Review, 15(4):243--267,
    Yorkcategory: C - Refereed Journal Paper,
    ID article: 2397


  365. T. Walsh. 2001. Search on High Degree Graphs. Proceedings of 17th IJCAI, IJCAI,
    Yorkcategory: D - Refereed International Conference Paper,
    ID article: 2492


  366. Miguel, I.. 2001. Symmetry-breaking in Planning: Schematic Constraints. Proceedings of the CP'01 Workshop on Symmetry in Constraints:17--24,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2400


  367. S.H. Muggleton. 2001. Statistical Aspects of Logic-Based Machine Learning. ACM Transactions on Computational Logic, Under revision,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/statilp.ps.gz ,
    ID article: 2647


  368. S. Muggleton. 2001. Learning from Positive data. Machine Learning, Accepted subject to revision,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/poslearn1.ps.gz ,
    ID article: 2646.

    Abstract:
    Gold showed in 1967 that not even regular grammars can be exactly identified from positive examples alone. Since it is known that children learn natural grammars almost exclusively from positives examples, Gold's result has been used as a theoretical support for Chomsky's theory of innate human linguistic abilities. In this paper new results are presented which show that within a Bayesian framework not only grammars, but also logic programs are learnable with arbitrarily low expected error from positive examples only. In addition, we show that the upper bound for expected error of a learner which maximises the Bayes' posterior probability when learning from positive examples is within a small additive term of one which does the same from a mixture of positive and negative examples. An Inductive Logic Programming implementation is described which avoids the pitfalls of greedy search by global optimisation of this function during the local construction of individual clauses of the hypothesis. Results of testing this implementation on artificially-generated data-sets are reported. These results are in agreement with the theoretical predictions.


  369. L.-V. Ciortuz. August 2001. Expanding feature-based constraint grammars: Experience on a large-scale HPSG grammar for English. Proceedings of the IJCAI 2001 co-located Workshop on Modelling and solving problems with constraints, Downloadable from sf http://www.lirmm.fr/~bessiere/proc\_wsijcai01.html., Seattle, USA,
    Yorkcategory: D - Refereed International Conference paper,
    ID article: 2456


  370. Lyndon Drake, Alan Frisch and Toby Walsh. 2001. Automatic Generation of Implied Clauses for SAT. Proceedings of the Seventh International Conference on Principles and Practice of Constraint Programming, Springer-Verlag, Lecture Notes in Computer Science, 2239,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2648


  371. M. Turcotte, S.H. Muggleton and M.J.E. Sternberg. 2001. Automated Discovery of Structural Signatures of Protein Fold and Function. Journal of Molecular Biology, 306(3):591--605,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/jmbfold.ps.gz ,
    ID article: 2643


  372. L.-V. Ciortuz. 2001. LIGHT AM -- Another Abstract Machine for Feature Structure Unification. Collaborative Language Engineering, Ed: D. Flickinger and S. Oepen and J. Tsujii and H. Uszkoreit, CSLI Publications, The Center for studies of Language, Logic and Information, Stanford University,
    Yorkcategory: B - part of book (chapter),
    ID article: 2459


  373. James Cussens. December 2001. Integrating Probabilistic and Logical Reasoning. Foundations of Bayesianism, Ed: David Corfield and Jon Williamson, Kluwer, Applied Logic Series, 24, Dordrecht,
    Yorkcategory: B - Part of Book (Chapter),
    ID article: 2440


  374. C.H. Bryant, S.H. Muggleton, S.G. Oliver and D.B. Kell. November 2001. Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes. Electronic Transactions on Artificial Intelligence, 6(12),
    http://www.ida.liu.se/ext/epa/cis/2001/012/tcover.html ,
    ID article: 2507


  375. M. De Boni. 2001. A Study on the Centrality of Relevance for Automated Question Answering, University of York,
    ID article: 2347


  376. Frisch, A.M., Miguel, I. and Walsh, T.. 2001. Symmetry and Implied Constraints in the Steel Mill Slab Design Problem. Proceedings of the CP'01 Workshop on Modelling and Problem Formulation:8--15,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2363


  377. Alan M. Frisch and Timothy J. Peugniez. August 2001. Solving Non-Boolean Satisfiability Problems with Stochastic Local Search. Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence:282-288, Seattle, Washington,
    Yorkcategory: D - Refereed International Workshop Paper,
    ID article: 2367


  378. Miguel, I. and Shen, Q.. 2001. Solution Techniques for Constraint Satisfaction Problems: Advanced Approaches. Artificial Intelligence Review, 15(4):269--293,
    Yorkcategory: C - Refereed Journal Paper,
    ID article: 2398


  379. S. Muggleton. 2001. Learning Stochastic Logic Programs. Electronic Transactions in Artificial Intelligence, 5(41),
    http://www.ida.liu.se/ext/epa/cis/2000/041/tcover.html ,
    ID article: 2641


  380. Nicos Angelopoulos and James Cussens. October 2001. Prolog issues of an MCMC algorithm. Proceedings of the 14th International Conference of Applications of Prolog:246--253, Tokyo,
    Yorkcategory: D - Refereed International Conference Paper,
    ID article: 2553


  381. Enrique Alfonseca, Marco De Boni, J.L. Jara-Valencia and Suresh Manandhar. 2001. A Prototype Question Answering System system using syntactic and semantic information for information retrieval. In Proceedings of TREC 10, National Institute of Standards and Technology,
    http://www-users.cs.york.ac.uk/~suresh/papers/SSIQA.pdf ,
    ID article: 3205


  382. Flener, P., Frisch, A.M., Hnich, B. and Kiziltan, Z.. 2001. Symmetry in Matrix Models. Proceedings of the CP'01 Workshop on Symmetry in Constraints:41--48,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2364


  383. P.G.K. Reiser, R.D. King, D.B. Kell and S.H. Muggleton. November 2001. Developing a Logical Model of Yeast Metabolism. Electronic Transactions in Artificial Intelligence, 6(24),
    http://www.ida.liu.se/ext/epa/cis/2001/024/tcover.html ,
    ID article: 2642


  384. Stephen Watkinson and Suresh Manandhar. 2001. Translating Treebank Annotation for Evaluation. Proceedings of the Workshop on Evaluation Methodologies for Language and Dialogue Systems, ACL/EACL 2001,
    Yorkcategory: D - Refereed International Workshop Paper,
    http://www-users.cs.york.ac.uk/~suresh/papers/TTAFE.pdf ,
    ID article: 2896


  385. Alonso, Eduardo. 2001. Adaptive Social Agents: Reactive vs Rational Architectures. Proceedings of the Seventh International Colloquium on Cognitive Science, Donostia-San Sebastián, Spain,
    ID article: 2533


  386. Ian Gent, Ewan MacIntyre, Patrick Prosser and Barbara Smith. 2001. Random Constraint Satisfaction: Flaws and Structure. Constraints, 6(4):345-372,
    Yorkcategory: C - Refereed Journal Paper,
    ID article: 2489


  387. L.-V. Ciortuz. October 2001. On compilation of the Quick-Check filter for feature structure unification. Proceedings of the IWPT 2001 International Workshop on Parsing Technologies, Beijing, China,
    Yorkcategory: D - Refereed International Conference paper,
    ID article: 2457


  388. Stephen Watkinson and Suresh Manandhar. 2001. Acquisition of Large Scale Categorial Grammar Lexicons. Proceedings of the Meeting of the Pacific Association for Computational Linguistics (PACLING),
    Yorkcategory: D - Refereed International Conference Paper,
    http://www.afnlp.org/archives/pacling2001/pdf/watkinson.pdf ,
    ID article: 3193


  389. Alistair Willis and Suresh Manandhar. 2001. The Availability of Partial Scopings in an Underspecified Semantic Representation. Computing Meaning (Volume 2), Ed: Harry Bunt and Reinhard Muskens and Elias Thijsse, Dordrecht: Kluwer, STUDIES IN LINGUISTICS AND PHILOSOPHY, 77,
    Yorkcategory: B - Part of Book (Chapter),
    http://www-users.cs.york.ac.uk/~suresh/papers/TAOPSIAUSR.pdf ,
    ID article: 2899


  390. T. Walsh. 2001. Permuation Problems and Channelling Constraints. Proceedings of 8th International Conference on Logic for Programming, Artificial Intelligence and Reasoning (LPAR 2001),
    Yorkcategory: D - Refereed International Conference Paper,
    ID article: 2493


  391. Miguel, I.. 2001. The Case for Dynamic Flexible Constraint Satisfaction. Proceedings of the CP'01 Workshop on Constraints and Uncertainty:19--20,
    Yorkcategory: E - Other Conference Paper,
    ID article: 2401


  392. Stephen Muggleton and John Firth. 2001. CProgol4.4: a tutorial introduction. Relational Data Mining, Ed: Saso Dzeroski and Nada Lavrac, Springer-Verlag,
    ID article: 2644


  393. Suresh Manandhar and Enrique Alfonseca. 2000. Noun Phrase chunking with APL2. In Proceedings of the APL-Berlin conference, To appear in ACM SIGAPL. Berlin, Germany,
    http://www-users.cs.york.ac.uk/~suresh/papers/NPChunkingAPL.pdf ,
    ID article: 3206


  394. Alonso, Eduardo and Kudenko, Daniel. 2000. Logic-Based Learning in Conflict Simulation Domains, ILCLI,
    http://www.cs.york.ac.uk/~ea/koldo.ps.gz ,
    ID article: 2531


  395. S. H. Muggleton and C. H. Bryant. 2000. Theory Completion using Inverse Entailment. Proceedings of the Tenth International Conference on Inductive Logic Programming, Ed: J. Cussens and A. Frisch, Springer Verlag http://www.springer.de/comp/lncs/index.html, Lecture Notes in Artificial Intelligence, London, UK,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_ilp2k.ps.gz ,
    ID article: 2635.

    Abstract:
    The main real-world applications of Inductive Logic Programming (ILP) to date involve the ``Observation Predicate Learning'' (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery and language learning potential applications exist in which OPL does not hold. OPL is ingrained within the theory and performance testing of Machine Learning. A general ILP technique called ``Theory Completion using Inverse Entailment'' (TCIE) is introduced which is applicable to non-OPL applications. TCIE is based on inverse entailment and is closely allied to abductive inference. The implementation of TCIE within Progol5.0 is described. The implementation uses contra-positives in a similar way to Stickel's Prolog Technology Theorem Prover. Progol5.0 is tested on two different data-sets. The first dataset involves a grammar which translates numbers to their representation in English. The second dataset involves hypothesising the function of unknown genes within a network of metabolic pathways. On both datasets near complete recovery of performance is achieved after relearning when randomly chosen portions of background knowledge are removed. Progol5.0's running times for experiments in this paper were typically under 6 seconds on a standard laptop PC.


  396. James Cussens and Stephen Pulman. September 2000. Incorporating Linguistics Constraints into Inductive Logic Programming. Proceedings of CoNLL2000 and LLL2000, ACL:184--193, Lisbon,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/jclll2000.ps.gz ,
    ID article: 2435.

    Abstract:
    We report work on effectively incorporating linguistic knowledge into grammar induction. We use a highly interactive bottom-up inductive logic programming (ILP) algorithm to learn `missing' grammar rules from an incomplete grammar. Using linguistic constraints on, for example, head features and gap threading, reduces the search space to such an extent that, in the small-scale experiments reported here, we can generate and store all candidate grammar rules together with information about their coverage and linguistic properties. This allows an appealingly simple and controlled method for generating linguistically plausible grammar rules. Starting from a base of highly specific rules, we apply least general generalisation and inverse resolution to generate more general rules. Induced rules are ordered, for example by coverage, for easy inspection by the user and at any point, the user can commit to a hypothesised rule and add it to the grammar. Related work in ILP and computational linguistics is discussed.


  397. Tamaddoni-Nezhad, A. and Muggleton, S. H.. 2000. Searching the Subsumption Lattice by a Genetic Algorithm. Proceedings of the 10th International Conference on Inductive Logic Programming, Ed: J. Cussens and A. Frisch, Springer-Verlag, Lecture Notes in Artificial Intelligence, 1866:243--252,
    ID article: 2649


  398. Alonso, Eduardo. 2000. From Artificial Intelligence to Multi-Agent Systems: Some Historical and Computational Remarks. Proceedings of the First Workshop on the History and Philosophy of Logic, Mathematics, and Computation (ILCLI), To be published by CSLI (Stanford University), Donostia, San Sebastián, Spain,
    http://www.cs.york.ac.uk/~ea/hplmc.ps.gz ,
    ID article: 2529


  399. S. H. Muggleton, C. H. Bryant, A. Srinivasan and A. Whittaker. 2000. Are grammatical representations useful for learning from biological sequence data? -- a case study, Department of Computer Science, Heslington, York, YO10 5DD, UK., University of York,
    ftp://ftp.cs.york.ac.uk/reports/YCS-2000-328.ps.gz ,
    ID article: 2638


  400. Savso Dvzeroski, James Cussens and Suresh Manandhar. 2000. An Introduction to Inductive Logic Programming and Learning Language in Logic. In Cussens and Dzeroski (Eds.), Learning Language in Logic, Springer, Ed: James Cussens and Savso Dvzeroski,
    http://www.springerlink.com/index/36a6xbj4q54dy1rv.pdf ,
    ID article: 3197.

    Abstract:
    This chapter introduces Inductive Logic Programming (ILP) and Learning Language in Logic (LLL). No previous knowledge of logic programming, ILP or LLL is assumed. Elementary topics are covered and more advanced topics are discussed. For example, in the ILP section we discuss subsumption, inverse resolution, least general generalisation, relative least general generalisation, inverse entailment, saturation, refinement and abduction. We conclude with an overview of this volume and pointers to future work.


  401. C. H. Bryant and S. H. Muggleton. 2000. Closed Loop Machine Learning, Department of Computer Science, Heslington, York, YO10 5DD, UK., University of York,
    ftp://ftp.cs.york.ac.uk/reports/YCS-2000-330.ps.gz ,
    ID article: 2506.

    Abstract:
    The aim of Closed Loop Machine Learning (CLML) is to partially automate some aspects of scientific work, namely the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. We have developed ASE-Progol (part of our CLML system) which uses ILP to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. We have developed a novel form of learning curve, which in contrast to the form of learning curve normally used in Active Learning, allows one to compare the costs incurred by different leaning strategies. We have applied ASE-Progol to a discovery task in Functional Genomics, the domain in which we aim to physically realise CLML. Although our work to date has been limited to a simplified model of this domain, the results have been encouraging. Parts of a model of Functional Genomics were removed and the ability of ASE-Progol to efficiently recover the performance of the model was measured. The cost of converging upon a hypothesis with an accuracy in the range 80-95\% was reduced if trials were selected by CLML rather than if they were sampled at random. To reach an accuracy in the range 80-87\%, CLML incurred over 10\% less experimental costs.


  402. S.H. Muggleton. 2000. Learning Stochastic Logic Programs. Proceedings of the AAAI2000 workshop on Learning Statistical Models from Relational Data, Ed: Lise Getoor and David Jensen, AAAI,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/slplearn.ps.gz ,
    ID article: 2633


  403. Ed: James Cussens and Saso Dzersoki. September 2000. Learning Language in Logic, Ed: James Cussens and Saso Dzersoki, Springer, LNAI State-of-the-Art Survey, LNAI, 1925, Berlin,
    ID article: 2432


  404. S. H. Muggleton, C. H. Bryant and A. Srinivasan. 2000. Learning Chomsky-like Grammars for Biological Sequence Families. Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, CA: Morgan Kaufmann, Stanford University, USA,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_icml2k.ps.gz ,
    ID article: 2636.

    Abstract:
    This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the positive-only learning framework of CProgol. Performance is measured using both predictive accuracy and a new cost function, Relative Advantage (RA). The RA results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. The highest RA was achieved by a model which includes grammar-derived features. This RA is significantly higher than the best RA achieved without the use of the grammar-derived features.


  405. James Cussens. 2000. Stochastic logic programs: Sampling, inference and applications. Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--2000), Morgan Kaufmann:115--122, San Francisco, CA,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/uai00.ps.gz ,
    ID article: 2436.

    Abstract:
    Algorithms for exact and approximate inference in stochastic logic programs (SLPs) are presented, based respectively, on variable elimination and importance sampling. We then show how SLPs can be used to represent prior distributions for machine learning, using (i) logic programs and (ii) Bayes net structures as examples. Drawing on existing work in statistics, we apply the Metropolis-Hasting algorithm to construct a Markov chain which samples from the posterior distribution. A Prolog implementation for this is described. We also discuss the possibility of constructing explicit representations of the posterior.


  406. Dimitar Kazakov. 2000. Achievements and Prospects of Learning Word Morphology with Inductive Logic Programming. Learning Language in Logic, Ed: James Cussens and Saso Dzeroski, Springer:89--109,
    http://www-users.cs.york.ac.uk/~kazakov/papers/lll.ps.gz ,
    ID article: 2389


  407. Stephen Watkinson and Suresh Manandhar. 2000. Unsupervised Lexical Learning with Categorial Grammars Using the LLL Corpus. In Cussens and Dzeroski (Eds.), Learning Language in Logic, Springer, Ed: James Cussens and Savso Dvzeroski, Expanded from citewat:99b, Lecture Notes in Artificial Intelligence,
    http://www.springerlink.com/index/cdbgm29338dvqlp8.pdf ,
    ID article: 3196


  408. Alonso, Eduardo and Kudenko, Daniel. 2000. Comments on Learning in Multi-Agent Systems. Proceedings of the Third Workshop of the UK Special Interest Group on Multi-Agent Systems (UKMAS-00), St. Catherine's College, Oxford, UK,
    ID article: 2530


  409. John C. Brown and Suresh Manandhar. 2000. Compilation versus abstract machines for fast parsing of typed feature structure grammars. Future Generation Computer Systems:771 - 791,
    http://www-users.cs.york.ac.uk/~suresh/papers/CVAMFFPOTFSG.pdf ,
    ID article: 2905


  410. S.H. Muggleton. 2000. Semantics and derivation for Stochastic Logic Programs. Proceedings of the UAI2000 workshop on Knowledge-Data Fusion, Ed: Richard Dybowski, UAI,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/slpsem.ps.gz ,
    ID article: 2634


  411. James Cussens and Stephen Pulman. 2000. Experiments in Inductive Chart Parsing. Learning Language in Logic, Ed: James Cussens and Saso Dzeroski, Springer, LNAI, 1925,
    ID article: 2433.

    Abstract:
    We use Inductive Logic Programming (ILP) within a chart-parsing framework for grammar learning. Given an existing grammar G, together with some sentences which G can not parse, we use ILP to find the ``missing'' grammar rules or lexical items. Our aim is to exploit the inductive capabilities of chart parsing, i.e. the ability to efficiently determine what is needed for a parse. For each unparsable sentence, we find actual edges and *needed edges*: those which are needed to allow a parse. The former are used as background knowledge for the ILP algorithm (P-Progol) and the latter are used as examples for the ILP algorithm. We demonstrate our approach with a number of experiments using context-free grammars and a feature grammar.


  412. James Cussens. 2000. Attribute-Value and Relational Learning: A Statistical Viewpoint. Proceedings of the ICML-2000 Workshop on Attribute-Value and Relational Learning: Crossing the Boundaries, Ed: De Raedt, Luc and Kramer, Stefan:35--39,
    http://www.informatik.uni-freiburg.de/~ml/icml2000_workshop/cussens.ps ,
    ID article: 2437.

    Abstract:
    In this extended abstract, rather than crossing the boundary between attribute-value and relational learning, we place ourselves above any such boundary and look down on the problem from the point of view of general principles of statistical inference. We do not pretend that this paper gives a full account of all relevant issues, but argue that starting from this generalised viewpoint and working down towards actual learning problems (e.g. decision tree learning, regression, ILP, etc) makes it easier to find the essential contrasts and similarities between different learning problems. Our primary goal (not achieved here) is to abstract away from superficial issues, such as the concrete syntactic representation of a problem or worse the sociological origin of an approach.


  413. Alonso, Eduardo and Kudenko, Daniel. 2000. Machine Learning Techniques for Logic-Based Multi-Agent Systems. Proceedings of the First Goddard Workshop on Formal Approaches to Agent-Based Systems, To be published in Lectures Notes on Computer Science, Springer-Verlag, NASA Goddard Space Flight Center, Greenbelt, MD, USA,
    http://www.cs.york.ac.uk/~ea/nasa.ps.gz ,
    ID article: 2528


  414. Ed: James Cussens and Alan Frisch. July 2000. Proceedings of the 10th Interrnational Conference on Inductive Logic Programming (ILP 2000), Ed: James Cussens and Alan Frisch, Springer, LNAI, 1866, London,
    http://link.springer.de/link/service/series/0558/tocs/t1866.htm ,
    ID article: 2434


  415. S. H. Muggleton, C. H. Bryant and A. Srinivasan. 2000. Measuring Performance when Positives are Rare: Relative Advantage versus Predictive Accuracy - a Biological Case-study. Proceedings of the 11th European Conference on Machine Learning, Ed: R. Lopez de Mantaras and E. Plaza, Springer Verlag, Lecture Notes in Computer Science, http://www.springer.de/comp/lncs/index.html,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_ecml2k.ps.gz ,
    ID article: 2637.

    Abstract:
    This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). Performance is measured using both predictive accuracy and a new cost function, Relative Advantage (RA). The RA results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. Predictive accuracy is not a good measure of performance for this domain because it does not discriminate well between NPP recognition models: despite covering varying numbers of (the rare) positives, all the models are awarded a similar (high) score by predictive accuracy because they all exclude most of the abundant negatives.


  416. M. De Boni, A. Grieson, D. Moore and D. Palmer-Brown. 2000. Proposed enhancements to a debating system. Proceedings of the Workshop on Computation Dialectics,
    ID article: 2349


  417. D. A. Duffy. 2000. Lemma-Generation and Rippling Tactics in the CADIZ Proof System, Department of Computer Science, Heslington, York, YO10 5DD, UK., University of York,
    ID article: 2512


  418. S. Muggleton and D. Page. 1999. A learnability model for universal representations and its application to top-down induction of decision trees. Machine Intelligence 15, Ed: K. Furukawa and D. Michie and S. Muggleton, Oxford University Press, In Press,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/utrees.ps.gz ,
    ID article: 2630


  419. James Cussens and Stephen Pulman. 1999. Experiments in Inductive Chart Parsing. Learning Language in Logic Workshop Notes (LLL99), Ed: James Cussens:72--83, Bled, Slovenia,
    http://www.cs.york.ac.uk/mlg/lll/workshop/proceedings/CussensPulman/CussensPulman.ps.gz ,
    ID article: 2429.

    Abstract:
    We use Inductive Logic Programming (ILP) within a chart-parsing framework for grammar learning. Given an existing grammar G, together with some sentences which G can not parse, we use ILP to find the ``missing'' grammar rules or lexical items. Our aim is to exploit the inductive capabilities of chart parsing, i.e. the ability to efficiently determine what is needed for a parse. For each unparsable sentence, we find actual edges and *needed edges*: those which are needed to allow a parse. The former are used as background knowledge for the ILP algorithm (P-Progol) and the latter are used as examples for the ILP algorithm. We demonstrate our approach with a number of experiments using context-free grammars and a feature grammar.


  420. Alonso, Eduardo. 1999. Derechos, coordinación y acción social en dominios multi-agentes. Inteligencia Artificial:3--12,
    http://www.cs.york.ac.uk/~ea/aepia2.ps ,
    ID article: 2523


  421. C. H. Bryant, S. H. Muggleton, C. D. Page and M. J. E. Sternberg. 1999. Combining Active Learning with Inductive Logic Programming to close the loop in Machine Learning. Proceedings of AISB'99 Symposium on AI and Scientific Creativity, Ed: S. Colton, The Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB):59--64, http://www.cogs.susx.ac.uk/aisb/,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_aisb99.ps.gz ,
    ID article: 2505.

    Abstract:
    Machine Learning (ML) systems that produce human-comprehensible hypotheses from data are typically open loop, with no direct link between the ML system and the collection of data. This paper describes the alternative, Closed Loop Machine Learning. This is related to the area of Active Learning in which the ML system actively selects experiments to discriminate between contending hypotheses. In Closed Loop Machine Learning the system not only selects but also carries out the experiments in the learning domain. ASE-Progol, a Closed Loop Machine Learning system, is proposed. ASE-Progol will use the ILP system Progol to form the initial hypothesis set. It will then devise experiments to select between competing hypotheses, direct a robot to perform the experiments, and finally analyse the experimental results. ASE-Progol will then revise its hypotheses and repeat the cycle until a unique hypothesis remains. This will be, to our knowledge, the first attempt to use a robot to carry out experiments selected by Active Learning within a real world application.


  422. Simon Anthony and Alan M. Frisch. 1999. Cautious Induction: An Alternative to Clause-at-a-time Induction in Inductive Logic Programming. New Generation Computing, 17(1):25-52,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/cautious.ps.gz ,
    ID article: 2665


  423. Alonso, Eduardo. 1999. Neither utilities nor norms: A preliminary report on rights in MAS. Proceedings of the Sixth International Colloquium on Cognitive Science (ICCS-99),
    http://www.cs.york.ac.uk/~ea/iccs99.ps ,
    ID article: 2526


  424. Dimitar Kazakov. 1999. Combining LAPIS and WordNet for the learning of LR parsers with optimal semantic constraints. The Ninth International Workshop ILP-99, Ed: Saso Dzeroski and Peter Flach, Springer-Verlag, Bled, Slovenia,
    http://www-users.cs.york.ac.uk/~kazakov/papers/kazakov-ILP99-lapis.ps.gz ,
    ID article: 2387


  425. Stephen Watkinson and Suresh Manandhar. 1999. Unsupervised Lexical Learning with Categorial Grammars using the LLL Corpus. In Inductive Logic Programming (ILP) Workshop on Logic Language and Learning (LLL), Bled, Slovenia,
    http://www.springerlink.com/index/cdbgm29338dvqlp8.pdf ,
    ID article: 3194


  426. Willis, Alistair and Manandhar, Suresh. 1999. Two Accounts of Scope Availability and Semantic Underspecification. Proceedings of the 37th annual meeting of the Association for Computational Linguistics,
    ID article: 3164


  427. S. Muggleton. 1999. Inductive Logic Programming: issues, results and the LLL challenge. Artificial Intelligence, 114(1),
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/aij99.ps.gz ,
    ID article: 2628.

    Abstract:
    Inductive Logic Programming (ILP) is the area of AI which deals with the induction of hypothesised predicate definitions from examples and background knowledge. Logic programs are used as a single representation for examples, background knowledge and hypotheses. ILP is differentiated from most other forms of Machine Learning (ML) both by its use of an expressive representation language and its ability to make use of logically encoded background knowledge. This has allowed successful applications of ILP in areas such as molecular biology and natural language which both have rich sources of background knowledge and both benefit from the use of an expressive concept representation languages. For instance, the ILP system Progol has recently been used to generate comprehensible descriptions of the 23 most populated fold classes of proteins, where no such descriptions had previously been formulated manually. In the natural language area ILP has not only been shown to have higher accuracies than various other ML approaches in learning the past tense of English but also shown to be capable of learning accurate grammars which translate sentences into deductive database queries. The area of Learning Language in Logic (LLL) is producing a number of challenges to existing ILP theory and implementations. In particular, language applications of ILP require revision and extension of a hierarchically defined set of predicates in which the examples are typically only provided for predicates at the top of the hierarchy. New predicates often need to be invented, and complex recursion is usually involved. Advances in ILP theory and implementation related to the challenges of LLL are already producing beneficial advances in other sequence-oriented applications of ILP. In addition LLL is starting to develop its own character as a sub-discipline


  428. Alan M. Frisch. 1999. Sorted Downward Refinement: Building Background Knowledge into a Refinement Operator for Inductive Logic Programming. Inductive Logic Programming: Proceedings of the Ninth International Conference, Ed: S. Dzeroski and P. Flach, Springer,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/ilp99.ps.gz ,
    ID article: 2359


  429. S. Muggleton and M. Bain. 1999. Analogical Prediction. Proc. of the 9th International Workshop on Inductive Logic Programming (ILP-99), Springer-Verlag:234--244, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ap.ps.gz ,
    ID article: 2631.

    Abstract:
    Inductive Logic Programming (ILP) involves constructing an hypothesis H on the basis of background knowledge B and training examples E. An independent test set is used to evaluate the accuracy of H. This paper concerns an alternative approach called Analogical Prediction (AP). AP takes B,E and then for each test example langle x,y angle forms an hypothesis H_x from B,E


  430. James Cussens, Saso Dzeroski and Tomaz Erjavec. June 1999. Morphosyntactic Tagging of Slovene using Progol. Inductive Logic Programming: Proc. of the 9th International Workshop (ILP-99), Ed: Saso Dzeroski and Peter Flach, Springer-Verlag, Bled, Slovenia,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/james.cussens/jcilp99.ps.gz ,
    ID article: 2430.

    Abstract:
    We consider the task of tagging Slovene words with morphosyntactic descriptions (MSDs). MSDs contain not only part-of-speech information but also attributes such as gender and case. In the case of Slovene there are 2,083 possible MSDs. P-Progol was used to learn morphosyntactic disambiguation rules from annotated data (consisting of 161,314 examples) produced by the MULTEXT-East project. P-Progol produced 1,148 rules taking 36 hours. Using simple grammatical background knowledge, e.g. looking for case disagreement, P-Progol induced 4,094 clauses in eight parallel runs. These rules have proved effective at detecting and explaining incorrect MSD annotations in an independent test set, but have not so far produced a tagger comparable to other existing taggers in terms of accuracy.


  431. Alonso, Eduardo. 1999. An individualistic approach to social action in Multi-Agent Systems. Journal of Experimental and Theoretical Artificial Intelligence, 11:519--530,
    http://www.cs.york.ac.uk/~ea/jetai.ps ,
    ID article: 2524


  432. K. Furukawa, D. Michie and S. Muggleton. 1999. Machine Intelligence 15: machine intelligence and inductive learning, Oxford University Press, In Press, Oxford,
    ID article: 2551


  433. R. Parson, K. Khan and S. Muggleton. 1999. Theory recovery. Proc. of the 9th International Workshop on Inductive Logic Programming (ILP-99), Springer-Verlag, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/trec.ps.gz ,
    ID article: 2632.

    Abstract:
    In this paper we examine the problem of repairing incomplete background knowledge using Theory Recovery. Repeat Learning under ILP considers the problem of updating background knowledge in order to progressively increase the performance of an ILP algorithm as it tackles a sequence of related learning problems. Theory recovery is suggested as a suitable mechanism. A bound is derived for the performance of theory recovery in terms of the information content of the missing predicate definitions. Experiments are described that use the logical back-propagation ability of Progol 5.0 to perform theory recovery. The experimental results are consistent with the derived bound.


  434. Stephen Watkinson and Suresh Manandhar. 1999. Unsupervised lexical learning of categorial grammars. In ACL'99 Workshop in Unsupervised Learning in Natural Language Proccesing,
    http://www-users.cs.york.ac.uk/~suresh/papers/ULLOCG.pdf ,
    ID article: 2943


  435. Alonso, Eduardo and Kudenko, Daniel. 1999. Machine Learning Techniques for Logic-Based Multi-Agent Systems. Proceedings of the Second Workshop of the UK Special Interest Group on Multi-Agent Systems (UKMAS-99), Hewlett-Packard Laboratories, Bristol, UK,
    http://www.cs.york.ac.uk/~ea/ukmas99.ps ,
    ID article: 2527


  436. Dimitar Kazakov, Suresh Manandhar and Tomavz Erjavec. 1999. Learning word segmentation rules for tag prediction. The Ninth International Workshop ILP-99, Ed: Savso Dvzeroski and Peter Flach, Springer-Verlag, Bled, Slovenia,
    http://www.springerlink.com/index/936r382628362554.pdf ,
    ID article: 3165


  437. James Cussens. 1999. Integrating Probabilistic and Logical Reasoning. Electronic Transactions on Artificial Intelligence, Selected Articles from the Machine Intelligence 16 Workshop, 3:79--103,
    http://www.ep.liu.se/ej/etai/1999/005/ ,
    ID article: 2428.

    Abstract:
    We examine the vexed question of connections between logical and probabilistic reasoning. The reasons for making such a connection are examined. We give an account of recent work which uses loglinear models to make the connection. We conclude with an analysis of various existing approaches combining logic and probability.


  438. Willis, Alistair and Manandhar, Suresh. January 1999. The Availability of Partial Scopings in an Underspecified Semantic Representation. Proceedings of the 3rd International Workshop on Computational Semantics (IWCS), Tilburg, the Netherlands,
    http://www-users.cs.york.ac.uk/~suresh/papers/TAOPSIAUSR.ps.gz ,
    ID article: 3192


  439. S. Muggleton. 1999. Inductive Logic Programming. The MIT Encyclopedia of the Cognitive Sciences (MITECS), Ed: Robert A. Wilson and Frank C. Keil, MIT Press,
    http://mitpress.mit.edu/MITECS/work/muggleton.html ,
    ID article: 2629


  440. Cussens, James. 1999. Loglinear models for first-order probabilistic reasoning. Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI--99), Morgan Kaufmann Publishers:126--133, San Francisco, CA,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/uai99.ps.gz ,
    ID article: 2431.

    Abstract:
    Recent work on loglinear models in probabilistic constraint logic programming is applied to first-order probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define the proof probabilities. We have a conservative extension of first-order reasoning, so that, for example, there is a one-one mapping between logical and random variables. We show how, in this framework, Inductive Logic Programming (ILP) can be used to induce the features of a loglinear model from data. We also compare the presented framework with other approaches to first-order probabilistic reasoning.


  441. Alonso, Eduardo. 1999. Inteligencia Artificial Distribuida: cómo entederla y usarla. Divulgación Científica,
    http://www.arrakis.es/~jjreina/revista/articulo/iad ,
    ID article: 2525


  442. S. Muggleton. 1999. Scientific Knowledge Discovery using Inductive Logic Programming. Communications of the ACM, 42(11):42--46,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/cacm2.ps.gz ,
    ID article: 2627.

    Abstract:
    This paper is an overview of scientific knowledge discovery tasks carried out using Inductive Logic Programming (ILP). The results reviewed have been published in some of the top general science journals, and as such are among the strongest examples of semi-automated scientific discovery in the Artificial Intelligence literature. Space restrictions do not permit this paper to cover other discovery areas of ILP. These include the discovery of linguistic features in natural language data and the discovery of patterns in traffic data.


  443. Frisch, Alan M.. April 1999. A Project to Build Background Knowledge into Refinement Operators for Inductive Logic Programming. Working Notes, 1999 AISB Workshop on Automated Reasoning: Bridging the Gap between Theory and Practice, Ed: Manfred Kerber, Edinburgh, Society for the Study of Artificial Intelligence and Simulation of Behavior,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/arw99.ps ,
    ID article: 2360


  444. Patrick Olivier, Jon Pickering, Nicolas Halper and Pamela Luna. April 1999. Visual Composition as Optimisation. AISB Symposium on AI and Creativity in Entertainment and Visual Art:22-30, Edinburgh, UK,
    ID article: 2563


  445. K. Khan, S. Muggleton and R. Parson. 1998. Repeat learning using predicate invention. Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), Ed: C.D. Page, Springer-Verlag, LNAI 1446:165--174, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/repeat.ps.gz ,
    ID article: 2621.

    Abstract:
    Most of machine learning is concerned with learning a single concept from a sequence of examples. In repeat learning the teacher chooses a series of related concepts randomly and independently from a distribution D. A finite sequence of examples is provided for each concept in the series. The learner does not initially know D, but progressively updates a posterior estimation of D as the series progresses. This papers considers predicate invention within Inductive Logic Programming as a mechanism for updating the learner's estimation of D. A new predicate invention mechanism implemented in Progol4.4 is used in repeat learning experiments within a chess domain. The results indicate that significant performance increases can be achieved. The paper develops a Bayesian framework and demonstrates initial theoretical results for repeat learning.


  446. S. Muggleton. 1998. Inductive Logic Programming: issues, results and the LLL challenge. Proceedings of ECAI98, Ed: H. Prade, John Wiley, Abstract of keynote talk:697,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ecai98.ps.gz ,
    ID article: 2616.

    Abstract:
    Inductive Logic Programming (ILP) citemugg:ilp,mugg:der is the area of AI which deals with the induction of hypothesised predicate definitions from examples and background knowledge. Logic programs are used as a single representation for examples, background knowledge and hypotheses. ILP is differentiated from most other forms of Machine Learning (ML) both by its use of an expressive representation language and its ability to make use of logically encoded background knowledge. This has allowed successful applications of ILP citebratmug:ilpapp in areas such as molecular biology citestern:roysoc,muggks:proteins,kmuggs:muta, Finn+Muggleton+Page+Srinivasan/98/Discovery and natural language citemooney:nlp,CusPagMugSri97:ECML97,Cus97-ILP97 which both have rich sources of background knowledge and both benefit from the use of an expressive concept representation languages. For instance, the ILP system Progol has recently been used to generate comprehensible descriptions of the 23 most populated fold classes of proteins citeturcotte:folds, where no such descriptions had previously been formulated manually. In the natural language area ILP has not only been shown to have higher accuracies than various other ML approaches in learning the past tense of English citemooney:foidl but also shown to be capable of learning accurate grammars which translate sentences into deductive database queries citezelle:semantics. In both cases, follow up studies citethompson:semantics,dzer:nominal have shown that these ILP approaches to natural language problems extend with relative ease to various languages other than English. The area of Learning Language in Logic (LLL) is producing a number of challenges to existing ILP theory and implementations. In particular, language applications of ILP require revision and extension of a hiera


  447. S. Muggleton, A. Srinivasan, R. King and M. Sternberg. 1998. Biochemical knowledge discovery using Inductive Logic Programming. Proc. of the first Conference on Discovery Science, Ed: H. Motoda, Springer-Verlag, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ds98jnt.ps.gz ,
    ID article: 2625.

    Abstract:
    Machine Learning algorithms are being increasingly used for knowledge discovery tasks. Approaches can be broadly divided by distinguishing discovery of procedural from that of declarative knowledge. Client requirements determine which of these is appropriate. This paper discusses an experimental application of machine learning in an area related to drug design. The bottleneck here is in finding appropriate constraints to reduce the large number of candidate molecules to be synthesised and tested. Such constraints can be viewed as declarative specifications of the structural elements necessary for high medicinal activity and low toxicity. The first-order representation used within Inductive Logic Programming (ILP) provides an appropriate description language for such constraints. Within this application area knowledge accreditation requires not only a demonstration of predictive accuracy but also, and crucially, a certification of novel insight into the structural chemistry. This paper describes an experiment in which the ILP system Progol was used to obtain structural constraints associated with mutagenicity of molecules. In doing so Progol found a new indicator of mutagenicity within a subset of previously published data. This subset was already known not to be amenable to statistical regression, though its complement was adequately explained by a linear model. According to the combined accuracy/explanation criterion provided in this paper, on both subsets comparative trials show that Progol's structurally-oriented hypotheses are preferable to those of other machine learning algorithms.


  448. Libor Jelínek, Dimitar Kazakov, Karel Malý and Olga Stepánková. 1998. Speech support for robot control. Eighth International Symposium on Measurement and Control in Robotics, Prague, Czech Republic,
    ID article: 2385


  449. R. Parson and S. Muggleton. 1998. An experiment with browsers that learn. Machine Intelligence 15, Ed: K. Furukawa and D. Michie and S. Muggleton, Oxford University Press, In Press,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/agents.ps.gz ,
    ID article: 2618


  450. James Cussens. August 1998. Notes on inductive logic programming methods in natural language processing (European work), Manuscript,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ilp98tut.ps.gz ,
    ID article: 2427.

    Abstract:
    The aim of these notes is to analyse ILP methods which have been applied to NLP, drawing exclusively on work conducted in Europe.


  451. Alonso, Eduardo. 1998. Rights and Coordination in Multi-Agent Systems. Proceedings of the First Workshop of the UK Special Interest Group on Multi-Agent Systems (UKMAS-98):18--25, Manchester, UK,
    http://www.cs.york.ac.uk/~ea/ukmas98.ps ,
    ID article: 2521


  452. S. Muggleton. 1998. Completing inverse entailment. Proceedings of the Eighth International Workshop on Inductive Logic Programming (ILP-98), Ed: C.D. Page, Springer-Verlag, LNAI 1446:245--249, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ie.ps.gz ,
    ID article: 2619.

    Abstract:
    Yamamoto has shown that the Inverse Entailment (IE) mechanism described previously by the author is complete for Plotkin's relative subsumption but incomplete for entailment. That is to say, an hypothesised clause H can be derived from an example E under a background theory B using IE if and only if H subsumes E relative to B in Plotkin's sense. Yamamoto gives examples of H for which Bcup H models E but H cannot be constructed using IE from B and E. The main result of the present paper is a theorem to show that by enlarging the bottom set used within IE, it is possible to make a revised version of IE complete with respect to entailment for Horn theories. Furthermore, it is shown for function-free definite clauses that given a bound k on the arity of predicates used in B and E, the cardinality of the enlarged bottom set is bounded above by the polynomial function p(c+1)^k, where p is the number of predicates in B,E and c is the number of constants in BcupoverlineE.


  453. I. Bratko, S. Muggleton and A. Karalic. 1998. Applications of Inductive Logic Programming. Machine Learning and Data Mining, Ed: R.S. Michalski and I. Bratko and M. Kubat, John Wiley and Sons Ltd., Chichester,
    ID article: 2617


  454. S. Roberts, W. Van Laerand, N. Jacobs and S. Muggleton. 1998. A comparison of ILP and propositional systems on propositional data. Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), Ed: C.D. Page, Springer-Verlag, LNAI 1446:291--299, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/traffic.ps.gz ,
    ID article: 2622.

    Abstract:
    This paper presents an experimental comparison of two Inductive Logic Programming algorithms, Progol and Tilde, with C4.5, a propositional learning algorithm, on a propositional dataset of road traffic accidents. Rebalancing methods are described for handling the skewed distribution of positive and negative examples in this dataset, and the relative cost of errors of commission and omission in this domain. It is noted that before the use of these methods all algorithms perform worse than majority class. On rebalancing, all did significantly better. The conclusion drawn from th experimental results is that on such a propositional data set ILP algorithms perform competitively in terms of predictive accuracy with propositional systems, but are significantly outperformed in terms of time taken for learning.


  455. Brown, John C. and Manandhar, Suresh. 1998. An Abstract Machine for Fast Parsing of Typed Feature Structure Grammars. In Workshop on Principles of Abstract Machines, From the Workshop on Principles of Abstract Machines, Pisa, September 1998, Pisa, September, University of Saarlandes,
    http://www-users.cs.york.ac.uk/~suresh/papers/AAMFFPOTFSG.pdf ,
    ID article: 3195


  456. Alistair Willis. 1998. Using Functional Structure for Probabilistic Semantic Disambiguation. ECAI 98 Conference Proceedings, Ed: Henri Prade, John Wiley and Sons Ltd.,
    ID article: 2650


  457. S. Muggleton. 1998. Knowledge discovery in biological and chemical domains. Proc. of the first Conference on Discovery Science, Ed: H. Motoda, Springer-Verlag, Abstract of keynote talk, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ds98inv.ps.gz ,
    ID article: 2626.

    Abstract:
    This talk will review the results of the last few years' academic pilot studies involving the application of ILP to the prediction of protein secondary structure, mutagenicity, structure activity , pharmacophore discovery and protein fold analysis. While predictive accuracy is the central performance measure of data analytical techniques which generate procedural knowledge (neural nets, decision trees, etc.), the performance of an ILP system is determined both by accuracy and degree of stereo-chemical insight provided. ILP hypotheses can be easily stated in English and exemplified diagrammatically. This allows cross-checking with the relevant biological and chemical literature. Most importantly it allows for expert involvement in human background knowledge refinement and for final dissemination of discoveries to the wider scientific community. In several of the comparative trials presented ILP systems provided significant chemical and biological insights where other data analysis techniques did not. In his statement of the importance of this line of research to the Royal Society Sternberg emphasised the aspect of joint human-computer collaboration in scientific discoveries. Science is an activity of human societies. It is our belief that computer-based scientific discovery must support strong integration into existing the social environment of human scientific communities. The discovered knowledge must add to and build on existing science. The author believes that the ability to incorporate background knowledge and re-use learned knowledge together with the comprehensibility of the hypotheses, have marked out ILP as a particularly effective approach for scientific knowledge discovery.


  458. Patrick Olivier. 1998. Kinematic Reasoning with Spatial Decompositions. Constraints,
    ID article: 2562


  459. Kazakov, Dimitar and Manandhar, Suresh. 1998. A Hybrid Approach to Word Segmentation. Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), Ed: Page, C. D., Springer-Verlag,
    http://www.springerlink.com/index/d6764838212184v8.pdf ,
    ID article: 3167


  460. S. Dzeroski, N. Jacobs, M. Molina and C. Moure. 1998. Detecting traffic problems with ILP. Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), Ed: C.D. Page, Springer-Verlag, LNAI 1446:281-290, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/spanish.ps.gz ,
    ID article: 2623.

    Abstract:
    Expert systems for decision support have recently been successfully introduced in road transport management. These systems include knowledge on traffic problem detection and alleviation. The paper describes experiments in automated acquisition of knowledge on traffic problem detection. The task is to detect road sections where a problem has occurred (critical sections) from sensor data. It is necessary to use inductive logic programming (ILP) for this purpose as relational background knowledge on the road network is essential. In this paper, we apply three state-pf-the-art ILP systems to learn how to detect traffic problems.


  461. Alonso, Eduardo. 1998. Groups and societies: One and the same thing?. Proceedings of the Sixth Iberoamerican Conference on Artificial Inteligence (IBERAMIA-98), Springer, Lectures Notes in Artificial Intelligence, 1484:52--63, Lisbon, Portugal,
    http://www.cs.york.ac.uk/~ea/iberamia.ps ,
    ID article: 2522


  462. C. H. Bryant and R. C. Rowe. 1998. Knowledge Discovery in Databases: Application to Chromatography. Trends in Analytical Chemistry, 17:18--24,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_TRAC.ps.gz ,
    ID article: 2504.

    Abstract:
    This paper reviews emerging computer techniques for discovering knowledge from databases and their application to various sets of separation data. The data-sets include the separation of a diverse range of analytes using either liquid, gas or ion chromatography. The main conclusion is that the new techniques should help to close the gap between the rate at which chromatographic data is gathered and stored electronically and the rate at which it can be analysed and understood.


  463. S. Muggleton. 1998. Advances in ILP theory and implementations. Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), Ed: C.D. Page, Springer-Verlag, Abstract of keynote presentation, LNAI 1446:9, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ilp98:invited.ps.gz ,
    ID article: 2624.

    Abstract:
    A strong linkage exists between advances in applications, implementations and theory within Inductive Logic Programming (ILP). Early ILP systems, such as FOIL, Golem and LINUS learned single predicate definitions from positive and negative examples and extensional background knowledge. They also employed strong learning biases such as ij-determinacy. Although these systems found a number of applications, they had problems in areas such as molecular biology and natural language learning. General mechanisms for inverting entailment have now been developed which support the use-of non-ground background knowledge, and the revision of multiple inter-related predicates. ILP theory results concerning complete refinement graph operators now allow efficient admissible searches. The absolute requirement for negative examples (rare within natural language domains) has been eased by Bayesian analysis of learning from positive-only examples. Bayesian approaches have also supported sample complexity analysis of predicate invention within the framework of repeat learning. In this framework it is assumed that the learner's prior is not equivalent to the distribution from which the teacher is sampling targets. By providing a series of sessions the learner is able to update the initial prior by adding and deleting background predicates. Within the Bayesian framework stochastic logic program representations have been used to estimate the distribution of examples over the instance space. Stochastic logic programs are a generalisation of hidden Markov models and stochastic grammars. Apart from a few special cases PAC-learning results have been largely negative for ILP. This is in large part due to the fact that testing satisfiability is intractrable for most interesting subsets of first-order Horn logic. The development of Bayesian approach


  464. P. Finn, S. Muggleton, D. Page and A. Srinivasan. 1998. Pharmacophore Discovery using the Inductive Logic Programming system Progol. Machine Learning, 30:241--271,
    ID article: 2615


  465. Dimitar Kazakov, Steve Pulman and Stephen Muggleton. 1998. The FraCaS dataset and the LLL challenge, Unpublished report,
    ID article: 2386


  466. Alonso, Eduardo. 1998. How individuals negotiate societies. Proceedings of the Third International Conference on Multi-Agent Systems (ICMAS-98), IEEE Computer Society Press:18--25, Paris, France,
    http://www.cs.york.ac.uk/~ea/icmas.ps ,
    ID article: 2520


  467. Manandhar, S., Dzeroski, S. and Erjavec T.. 1998. Learning Multilingual Morphology with CLOG. Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), Ed: Page, C.D., Springer-Verlag,
    http://www.springerlink.com/index/mqx06152v6t11061.pdf ,
    ID article: 3168


  468. James Cussens. 1998. Using Prior Probabilities and Density Estimation for Relational Classification. Inductive Logic Programming: Proceedings of the 8th International Conference (ILP-98), Ed: David Page, Springer, Lecture Notes in Artificial Intelligence, 1446:106--115,
    ID article: 2426


  469. M. Turcotte, S. H. Muggleton and M. J. E. Sternberg. 1998. Protein Fold Recognition. Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), Ed: C.D. Page, Springer-Verlag, LNAI 1446:53--64, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/folds.ps.gz ,
    ID article: 2620.

    Abstract:
    Inductive Logic Programming (ILP) has been applied to discover rules governing the three-dimensional topology of protein structure. The data-set unifies two sources of information; SCOP and PROMOTIF. Cross-validation results for experiments using two background knowledge sets, global (attribute-valued) and constitutional (relational), are presented. The application makes use of a new feature of Progol4.4 for numeric parameter estimation. At this early stage of development, the rules produced can only be applied to proteins for which the secondary structure is known. However, since the rules are insightful, they should prove to be helpful in assisting the development of taxonomic schemes. The application of ILP to fold recognition represents a novel and promising approach to this problem.


  470. Hugh Osborne and Derek Bridge. 1997. We're All Going on a Summer Holiday: An Exercise in Non-Cardinal Case Base Retreival. Proceedings of the 6th Scandinavian Conference on Artificial Intelligence (SCAI'97),
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/scai97.ps.gz ,
    ID article: 2539


  471. Simon Anthony and Alan M. Frisch. 1997. Cautious Induction in Inductive Logic Programming. Proceedings of the 7th International Workshop on Inductive Logic Programming, Ed: N. Lavrac and S. Dzeroski, Springer Verlag, Prague, Czech Republic,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/cils.ps.gz ,
    ID article: 2662


  472. Dimitar Kazakov. 1997. Unsupervised learning of naive morphology with genetic algorithms. Workshop Notes of the ECML/MLnet Workshop on Empirical Learning of Natural Language Processing Tasks, Ed: W. Daelemans, A. van~den Bosch and A. Weijters:105-112, Prague, Czech Republic,
    http://www-users.cs.york.ac.uk/~kazakov/papers/published-ga2.ps ,
    ID article: 2381


  473. Patrick Olivier. 1997. Co-ordinating the Visual and Verbal Domains. ACM Workshop on Perceptual User Interfaces, Banff, Alberta, Canada,
    ID article: 2561


  474. James Cussens. 1997. Part-of-Speech Tagging using Progol. Inductive Logic Programming: Proceedings of the 7th International Workshop (ILP-97). LNAI 1297, Springer:93--108,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ilp97.ps.gz ,
    ID article: 2424.

    Abstract:
    A system for `tagging' words with their part-of-speech (POS) tags is constructed. The system has two components: a lexicon containing the set of possible POS tags for a given word, and rules which use a word's context to eliminate possible tags for a word. The Inductive Logic Programming (ILP) system Progol is used to induce these rules in the form of definite clauses. The final theory contained 885 clauses. For background knowledge, Progol uses a simple grammar, where the tags are terminals and predicates such as t nounp (noun phrase) are nonterminals. Progol was altered to allow the caching of information about clauses generated during the induction process which greatly increased efficiency. The system achieved a per-word accuracy of 96.4\% on known words drawn from sentences without quotation marks. This is on a par with other tagging systems induced from the same data citeDaeZavBerGil96-WVLC96,Bri94-AAAI94,CutKupPedSib92-ANLP92 which all have accuracies in the range 96--97\%. The per-sentence accuracy was 49.5\%.


  475. Alonso, Eduardo. 1997. An uncompromising individualistic formal model of social activity. Working Notes of the Second UK Workshop on Foundations of Multi-Agent Systems (FoMAS-97), Ed: M. Luck and M. Fisher and M. d'Inverno and N. Jennings and Wooldridge, M.:21--32, Warwick, UK,
    http://www.cs.york.ac.uk/~ea/fomas.ps ,
    ID article: 2518


  476. D. K. G. Campbell, H. R. Osborne, A. M. Wood and D. G. Bridge. 1997. Parallel Case Base Retrieval: an Implementation on Distributed Linda. Proceedings of the 9th International Conference on Parallel and Distributed Computing and Systems (PDCS'97),
    ID article: 2541


  477. C. H. Bryant. 1997. Data Mining via ILP: The Application of Progol to a Database of Enantioseparations.. Proceedings of the Seventh International Workshop on Inductive Logic Programming, Ed: N. Lavrac and S. Dzeroski, Springer Verlag, Lecture Notes in Artificial Intelligence(1297):85--92,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_ilp97.ps.gz ,
    ID article: 2502.

    Abstract:
    As far as this author is aware, this is the first paper to describe the application of Progol to enantioseparations. A scheme is proposed for data mining a relational database of published enantioseparations using Progol. The application of the scheme is described and a preliminary assessment of the usefulness of the resulting generalisations is made using their accuracy, size, ease of interpretation and chemical justification.


  478. Ed: S. Muggleton. 1997. Proceedings of the Sixth International Workshop on Inductive Logic Programming, Ed: S. Muggleton, Springer-Verlag, LNAI 1314, Berlin,
    ID article: 2610


  479. Patrick Olivier. 1997. Hierarchy and Attention in Computational Imagery. Machine Graphics and Vision, 6(1):77-88,
    ID article: 2559


  480. S. Moyle and S. Muggleton. 1997. Learning programs in the event calculus. Proceedings of the Seventh Inductive Logic Programming Workshop (ILP97), Ed: N. Lavrac and S. Dzeroski, Springer-Verlag, LNAI 1297:205--212, Berlin,
    ID article: 2614


  481. Hugh Osborne and Derek Bridge. 1997. Similarity Metrics: A Formal Unification of Cardinal and Non-Cardinal Similarity Measures. Proceedings of the 2nd International Conference on Case-based Reasoning (ICCBR-97),
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/iccbr2.ps.gz ,
    ID article: 2540


  482. Simon Anthony and Alan M. Frisch. 1997. Generating Numerical Literals During Refinement. Proceedings of the 7th International Workshop on Inductive Logic Programming, Ed: N. Lavrac and S. Dzeroski, Springer Verlag, Prague, Czech Republic,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/num.ps.gz ,
    ID article: 2663


  483. Anthony D. Griffiths and Derek G. Bridge. July 1997. PAC Analyses of a `Similarity Learning' IBL Algorithm. Case-Based Reasoning Research and Development: Proceedings of the Second International Conference on Case-Based Reasoning, Ed: Leake, D.B. and Plaza, E., Springer Verlag, Lecture Notes in Artificial Intelligence, 1266:445-454,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/ICCBR2_PAC.ps.gz ,
    ID article: 2671


  484. Dimitar Kazakov, Libor Jelínek, Karel Malý and Olga Stepánková. 1997. Man-robot natural language interaction project---a year later, Prague, Czech Republic, The Gerstner Laboratory for Intelligent Decision Making, Czech Technical Universit,
    ID article: 2383


  485. Duncan K. G. Campbell, Hugh R. Osborne and Alan M. Wood. 1997. Characterising the Design Space for Linda Semantics, University of York,
    http://www.cs.york.ac.uk/isg/papers/hugh.osborne/lindasemantics.ps.gz ,
    ID article: 2542


  486. A. Srinivasan, R.D. King S.H. Muggleton and M. Sternberg. 1997. Carcinogenesis predictions using ILP. Proceedings of the Seventh International Workshop on Inductive Logic Programming, Ed: N. Lavrac and S. Dzeroski, Springer-Verlag, LNAI 1297:273--287, Berlin,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ilp97a.ps.gz ,
    ID article: 2612


  487. David A. Duffy, Alan M. Frisch and Ian Toyn. 1997. A Project to Develop an Inductive Proof Assistant for Z Integrating Classical and Rewrite Strategies, ( unpublished),
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/induction-project.ps.gz ,
    ID article: 2511


  488. James Cussens, David Page, Stephen Muggleton and Ashwin Srinivasan. 1997. Using Inductive Logic Programming for Natural Logic Processing. ECML'97 -- Workshop Notes on Empirical Learning of Natural Language Tasks, Ed: W. Daelemans and T. Weijters and A. van der Bosch, University of Economics, Invited keynote paper:25--34, Prague,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ecml97mlnet.ps.gz ,
    ID article: 2425


  489. Alonso, Eduardo. 1997. A logical representation of a negotiation protocol for autonomous agents. Proceedings of the International Workshop ``Distributed Artificial Intelligence and Multi-Agent Systems'':32--43, St. Petersburg, Russia,
    http://www.cs.york.ac.uk/~ea/daimas.ps ,
    ID article: 2519


  490. David A. Duffy, Alan M. Frisch and Ian Toyn. April 1997. Proof by Induction: Bridging the Gap between Proof Theory and Practical Automated Proof Systems. Working Notes, 1997 AISB Workshop on Automated Reasoning: Bridging the Gap between Theory and Practice, Ed: Michael Fisher, Manchester,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/bridging97.ps.gz ,
    ID article: 2510


  491. S. Muggleton. 1997. Declarative knowledge discovery in industrial databases. Proceedings of the First International Conference and Exhibition on The Practical Application of Knowledge Discovery and Data Mining (PADD-97), Ed: H.F. Arner, Practical Application Company Ltd.:9--24,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/padd97.ps.gz ,
    ID article: 2611


  492. Patrick Olivier. 1997. Estimating Visuospatial Properties in Graphics. British Computer Society SGES Expert Systems Conference, Cambridge, UK,
    ID article: 2560


  493. Jochen Dorre and Suresh Manandhar. 1997. On Constraint-based Lambek Calculi. Specifying Syntactic Structures, Ed: Patrick Blackburn and Martin de Rijke, CSLI Publications, Available from the CMP-LG archive http://xxx.soton.ac.uk/archive/ cmp-lg, Studies in Logic, Language and Information, chapter: 2:25-44, Center for the Study of Language and Information, Ventura Hall, Stanford, CA 94305,
    http://www-users.cs.york.ac.uk/~suresh/papers/CSLICBLC.pdf ,
    ID article: 3207


  494. Libor Jelínek and Dimitar Kazakov. 1997. A prototype of multi-level spoken language processing. The Seventh Czech-German Workshop on Speech Processing, Prague, Czech Republic,
    ID article: 2382


  495. Alonso, Eduardo. 1997. A Formal Framework for the Representation of Negotiation Protocols. Inteligencia Artificial:30--49,
    ID article: 2517


  496. Hugh Osborne and Derek Bridge. 1997. A Formal Analysis of Case Base Retrieval, University of York,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/tr-1.ps.gz ,
    ID article: 2543


  497. Bryant, C. H., Adam, A. E., Taylor, D. R. and Rowe, R. C.. 1997. Using Inductive Logic Programming to Discover Knowledge Hidden in Chemical Data.. Chemometrics and Intelligent Laboratory Systems, 36(2):111--123,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_golem.ps.gz ,
    ID article: 2500.

    Abstract:
    This paper demonstrates how general purpose tools from the field of Inductive Logic Programming (ILP) can be applied to analytical chemistry. As far as these authors are aware, this is the first published work to describe the application of the ILP tool Golem to separation science. An outline of the theory of ILP is given, together with a description of Golem and previous applications of ILP. The advantages of ILP over classical machine induction techniques, such as the Top-Down-Induction-of-Decision-Tree family, are explained. A case-study is then presented in which Golem is used to induce rules which predict, with a high accuracy (82\%), whether each of a series of attempted separations succeed or fail. The separation data was obtained from published work on the attempted separation of a series of 3-substituted phthalide enantiomer pairs on (R)-N-(3,5-dinitrobenzoyl)-phenylglycine.


  498. Simon Anthony and Alan M. Frisch. 1997. Using Meta-Languages for Learning. Area Meeting of CompulogNet:Computational Logic and Machine Learning, Ed: Flach, P. and Lavrac, N.:4--7, Prague, Czech Republic, CompulogNet,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/rep.ps.gz ,
    ID article: 2664


  499. West, M. M., Bryant, C. H. and McCluskey, T. L.. 1997. Transforming General Program Proofs: A Meta Interpreter which Expands Negative Literals. The preliminary Proceedings of the Seventh International Workshop on Logic Program Synthesis and Transformation, Leuven, Belgium,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_lopstr97.ps.gz ,
    ID article: 2503


  500. C. H. Bryant. April 1997. Computer Generation of Rules for an Expert System for Enantioseparations., Invited presentation given at Chrial Technology and Enatioseparations '97, Cambridge, UK,
    ID article: 2501


  501. A. Srinivasan, R.D. King S.H. Muggleton and M. Sternberg. 1997. The predictive toxicology evaluation challenge. Proceedings of the Fifteenth International Joint Conference Artificial Intelligence (IJCAI-97), Morgan-Kaufmann:1--6,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ijcai97.ps.gz ,
    ID article: 2613


  502. Nakata, K., Olivier, P., Bill, J. and Boyce, D.. 1996. Matching and Tracking Using Spatial Decomposition. Machine GRAPHICS & VISION, 5(1):131-140,
    ID article: 2556


  503. N Lavrac, D Zupanic, I Weber and D Kazakov. 1996. ILPNET repositories on WWW: Inductive Logic Programming Systems, Datasets and Bibliography. AI Communications, 9(4),
    http://www.dbai.tuwien.ac.at/AICOM/ ,
    ID article: 2377


  504. Cussens, James. 1996. Bayesian Inductive Logic Programming with Explicit Probabilistic Bias, PRG-TR-24-96, Oxford University Computing Laboratory,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/prg-tr-24-96.ps.gz ,
    ID article: 2422


  505. James Cussens. July 1996. Deduction, Induction and Probabilistic Support. Synthese, 108(1):1--10,
    ID article: 2419.

    Abstract:
    Elementary results concerning the connections between deductive relations and probabilistic support are given. These are used to show that Popper-Miller's result is a special case of a more general result, and that their result is not ``very unexpected'' as claimed. According to Popper-Miller, a purely inductively supports b only if they are ``deductively ind-ep-en-dent''---but this means that eg a vdash b. Hence, it is argued that viewing induction as occurring only in the absence of deductive relations, as Popper-Miller sometimes do, is untenable. Finally, it is shown that Popper-Miller's claim that deductive relations determine probabilistic support is untrue. In general, probabilistic support can vary greatly with fixed deductive relations as determined by the relevant Lindenbaum algebra.


  506. Alonso, Eduardo. 1996. Agentes Locales y Autónomos en Inteligencia Artificial Distribuida: Coordinación, UPV-EHU,
    ID article: 2515


  507. Anthony D. Griffiths. September 1996. Inductive Generalisation in Case-Based Reasoning Systems, Department of Computer Science, University of York,
    http://www.cs.york.ac.uk/isg/papers/tony.griffiths/phdthesis.ps.Z ,
    ID article: 2670


  508. S.H. Muggleton. 1996. Stochastic logic programs. Advances in Inductive Logic Programming, Ed: L. de Raedt, IOS Press:254--264,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/slp.ps.gz ,
    ID article: 2608


  509. Bryant, C.H., Adam, A.E., Taylor, D.R. and Rowe, R.C.. 1996. Towards an Expert System for Enantioseparations: Induction of Rules Using Machine Learning.. Chemometrics and Intelligent Laboratory Systems, 34(1):21--40,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_DataMariner.ps.gz ,
    ID article: 2498.

    Abstract:
    A commercially available machine induction tool was used in an attempt to automate the acquisition of the knowledge needed for an expert system for enantioseparations by High Performance Liquid Chromatography using Pirkle-type chiral stationary phases (CSPs). Various rule-sets were induced that recommended particular CSP chiral selectors based on the structural features of an enantiomer pair. The results suggest that the accuracy of the optimal rule-set is 63\% + or - 3\% which is more than ten times greater than the accuracy that would have resulted from a random choice.


  510. Jochen D. 1996. A Report on the Draft EAGLES Encoding Standard for HPSG. 3rd International Conference on HPSG and TALN-96: Traitement Automatique du Langage Naturel:161-168, Marseille, France,
    http://www-users.cs.york.ac.uk/~suresh/papers/AROTDEESFH.ps.gz ,
    ID article: 2931


  511. R. King, S. Muggleton, A. Srinivasan and M. Sternberg. 1996. Structure-activity relationships derived by machine learning: the use of atoms and their bond connectives to predict mutagenicity by inductive logic programming. Proceedings of the National Academy of Sciences, 93:438--442,
    ID article: 2605


  512. A. Srinivasan, S. Muggleton, R. King and M. Sternberg. 1996. Theories for mutagenicity: a study of first-order and feature based induction. Artificial Intelligence, 85(1):277--299,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ash_aij95.ps.gz ,
    ID article: 2607


  513. Suresh Manandhar. 1996. The EAGLES Encoding format for HPSG. Expert Advisory Group for Language Engineering Standards (EAGLES), Formalisms Working Group, Report, Ed: EAGLES, European Commission, Available from EAGLES homepage http://www.ilc.pi.cnr.it/EAGLES/home.html,
    http://www-users.cs.york.ac.uk/~suresh/papers/TEEFFH.ps.gz ,
    ID article: 2919


  514. Hugh Osborne and Derek Bridge. April 1996. Parallel Retrieval from Case Bases. Proceedings of the Second UK Case-Based Reasoning Workshop, Salford, England,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/salford.ps.gz ,
    ID article: 2537


  515. D. Duffy, C. MacNish and M. Osborne. 1996. An Integrated Framework for Analysing Changing Requirements, Unpublished, Department of Computer Science, University of York,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/Proteus/jpaper.ps.gz ,
    ID article: 2509


  516. S. Muggleton, C.D. Page and A. Srinivasan. 1996. An initial experiment into stereochemistry-based drug design using ILP. Proceedings of the Sixth Inductive Logic Programming Workshop (ILP96), Ed: S. Muggleton, Springer-Verlag, LNAI 1314:25--40, Berlin,
    ID article: 2609


  517. Dimitar Kazakov. 1996. An inductive approach to natural language parser design. Proceedings of NeMLaP-2, Ed: Kemal Oflazer and Harold Somers:209-217, Bilkent University, Ankara, Turkey,
    ID article: 2378


  518. Libor Jelínek and Dimitar Kazakov. 1996. Man-robot natural language interaction, The Gerstner Laboratory for Intelligent Decision Making, Czech Technical Universit,
    ID article: 2379


  519. Cussens, James. 1996. Part-of-speech disambiguation using ILP, Oxford University Computing Laboratory,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/prg-tr-25-96.ps.gz ,
    ID article: 2423


  520. James Cussens. August 1996. Machine Learning. IEE Journal of Computing and Control, 7(4):164--168,
    ID article: 2420


  521. Alonso, Eduardo and Kudenko, Daniel. 1996. A Formal Framework for the Representation of Negotiation Protocols, An abridged version appeared in Inteligencia Artificial 3/97, pp. 30-49. 1997, ILCLI,
    http://www.cs.york.ac.uk/~ea/ilcli.ps ,
    ID article: 2516


  522. Ed: Patrick Olivier. 1996. Proceedings of the ECAI-96 Workshop on the Representation and Processing of Spatial Expressions, Ed: Patrick Olivier, Budapest, Hungary, European Conference on Artificial Intelligence,
    ID article: 2558


  523. T. L. McCluskey, J. M. Porteous, M. M. West and C. H. Bryant. September 1996. The Validation of Formal Specifications of Requirements. Proceedings of the BCS-FACS Northern Formal Methods Workshop, Electronic Workshops in Computing Series, Springer, Ilkley, UK,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_north_fm_ws.ps.gz ,
    ID article: 2499


  524. Suresh Manandhar. 1996. Proceedings of Computational Logics for Natural Language Processing (CLNLP). Edinburgh,
    http://www-users.cs.york.ac.uk/~suresh/papers/POCLFNLP(.pdf ,
    ID article: 2920


  525. Hugh Osborne and Derek Bridge. 1996. A Case Base Similarity Framework. Advances in Case-Based Reasoning Proceedings of EWCBR'96, Ed: Ian Smith and Boi Faltings:309--323,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/ewcbr3.ps.gz ,
    ID article: 2538


  526. Brown, J. C., Chase, M. R., Kirkwood, P. J. and Sadik, A. K.. November 1996. A Postscript Document Management System. Proceedings of the 11th International Symposium on Computer and Information Sciences:675-684, Antalya, Turkey,
    ID article: 2547


  527. Anthony, Simon and Frisch, Alan M.. April 1996. Towards Inductive Constraint Logic Programming. Working Notes, 1996 AISB Workshop on Automated Reasoning: Bridging the Gap between Theory and Practice, Ed: Ian Gent:3--4, Brighton, Society for the Study of Artificial Intelligence and Simulation of Behavior,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/iclp_proposal.ps.gz ,
    ID article: 2661


  528. Anthony D. Griffiths and Derek G. Bridge. April 1996. A Yardstick for the Evaluation of Case-Based Classifiers. Procs. of Second U.K. Workshop on Case-Based Reasoning, Salford, UK,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/UK2.ps.gz ,
    ID article: 2669


  529. Olivier, P., Ormsby, A. and Nakata, K.. 1996. Occupancy Array-based Kinematic Reasoning. Engineering Applications of Artificial Intelligence, 9(5):541-549,
    ID article: 2557


  530. Dimitar Kazakov. 1996. Inductive Learning of LR parsers from treebanks, ISSN 0751-1345, ENST, Paris,
    ID article: 2380


  531. James Cussens. 1996. Effective Sample Size in a Dichotomous Process with Noise. Communications in Statistics: Theory and Methods, 25(6):1233--1246,
    ID article: 2421.

    Abstract:
    The effect of noise in a dichotomous process is studied from the Bayesian viewpoint. Winkler's approximation to the posterior distribution in the presence of noise is shown to break down badly near the limits of its application. Information loss is measured using effective sample size. An account of the relationship between effective sample size/information loss and sampling data is given which differs sharply from that of previous work in this area.


  532. Hugh Osborne. 1996. Update Plans for Parallel Architectures. Abstract Machine Models for Parallel and Distributed Computing (Proceedings of the Third Abstract Machines Workshop), IOS-press, Amsterdam,
    http://www.cs.york.ac.uk/isg/papers/hugh.osborne/AMW.ps.Z ,
    ID article: 2536


  533. S. Muggleton and D. Michie. 1996. Machine intelligibility and the duality principle. British Telecom Technology Journal, 14(4):15--23,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/bttj.ps.gz ,
    ID article: 2606


  534. I. Bratko and S. Muggleton. 1995. Applications of Inductive Logic Programming. Communications of the ACM, 38(11):65--70,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/cacm.ps.gz ,
    ID article: 2603


  535. Bryant, C. H., Adam, A. E., Taylor, D. R. and Conroy, G. V.. 1995. DataMariner, a Commercially Available Data Mining Package, and its Application to a Chemistry Domain.. Data Mining, London, UK, UNICOM,
    ID article: 2496


  536. Brown, John C.. December 1995. High Speed Feature Unification and Parsing. Natural Language Engineering:309-338,
    ID article: 2545


  537. K. Furukawa, D. Michie and S. Muggleton. 1995. Machine Intelligence 14: machine intelligence and inductive learning, Oxford University Press, Oxford,
    ID article: 2550


  538. S. Muggleton. 1995. Inverse entailment and Progol. New Generation Computing, 13:245--286,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/InvEnt.ps.gz ,
    ID article: 2602


  539. Frisch, Alan M. and Dumbill, Edmund J. A.. April 1995. Solving Constraint Satisfaction Problems with MV-Resolution: Initial Investigations. Working Notes, 1995 AISB Workshop on Automated Reasoning: Bridging the Gap between Theory and Practice, Ed: Andrew Ireland:35--36, Sheffield, Society for the Study of Artificial Intelligence and Simulation of Behavior,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/bridging95.ps.gz ,
    ID article: 2357


  540. G. Erbach, M. van der Kraan, S. Manandhar and H. Ruessink. October 1995. Extending Unification Formalisms. Second Language Engineering Convention, Available from the CMP-LG archive http://xxx.soton.ac.uk/archive/cmp-lg, London, England,
    http://www-users.cs.york.ac.uk/~suresh/papers/EUF.pdf ,
    ID article: 3208


  541. Suresh Manandhar. 1995. An Attributive Logic of Set Descriptions and Set Operations: Extended Report.. Edinburgh Working Papers in Cognitive Science, Volume 10, University of Edinburgh:39-60,
    http://www-users.cs.york.ac.uk/~suresh/papers/EWPCS.pdf ,
    ID article: 3181


  542. Brown, J. C. and Sadik, A. K.. 1995. Cataloguing, Indexing, Searching and Browsing Multiple Postscript Documents. The New Review of Document and Text Management, 1:215-236,
    ID article: 2546


  543. Anthony D. Griffiths and Derek G. Bridge. 1995. Inductive bias in case-based reasoning systems, Department of Computer Science, University of York,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/YCS-95-259.ps.gz ,
    ID article: 2668


  544. Bryant, C. H., Adam, A. E., Taylor, D. R. and Conroy, G. V.. 1995. Discovering Knowledge Hidden in a Chemical Database Using a Commercially Available Data Mining Tool.. Knowledge Discovery in Databases, IEE Computing and Control Division, Savoy Place, London, WC2R OBL, UK.,
    ID article: 2497


  545. Gregor Erbach and Suresh Manandhar. 1995. Visions for the Future of Logic-Based Natural Language Processing. Proceedings of the International Logic Programming Symposuim Workshop Visions for the Future of Logic Programming, Portland, Oregon, USA,
    ID article: 3191


  546. M. G. J. van den Brand, S. M. Eijkelkamp, D. K. A. Geluk and H. Meijer. 1995. Program Transformations using ASF+SDF. ASF+SDF`95: a workshop on Generating Tools from Algebraic Specifications, Programming Research Group, University of Amsterdam,
    ID article: 2535


  547. Anthony D. Griffiths and Derek G. Bridge. 1995. On Concept Space and Hypothesis Space in Case-Based Learning Algorithms. Machine Learning: ECML-95 (Proc. of the Eighth European Conference on Machine Learning), Ed: Nada Lavrac and Stefan Wrobel, Springer-Verlag:161--173,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/ECML_95.ps.gz ,
    ID article: 2666


  548. S. Muggleton. 1995. Inverting entailment and Progol. Machine Intelligence 14, Ed: K. Furukawa and D. Michie and S. Muggleton, Oxford University Press,
    ID article: 2604


  549. Frisch, Alan M. and Page Jr., C. David. August 1995. Building Theories into Instantiation. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence:1210--1216, Montreal, Canada,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/ordering.ps.gz ,
    ID article: 2358


  550. James Cussens. 1995. A Bayesian Analysis of Algorithms for Learning Finite Functions. Machine Learning: Proceedings of the Twelfth International Conference (ML95), Ed: Armand Prieditis and Stuart Russell, Morgan Kaufmann Publishers:142--149, San Francisco, CA,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ml95.ps.gz ,
    ID article: 2418.

    Abstract:
    We consider algorithms for learning functions f: X ightarrow Y, where X and Y are finite, and there is assumed to be no noise in the data. Learning algorithms, alg, are connected with galg, the set of prior probability distributions for which they are optimal. A method for constructing galg from alg is given and the relationship between the various galg is discussed. Improper algorithms are identified as those for which galg has zero volume. Improper algorithms are investigated using linear algebra and two examples of improper algorithms are given. This framework is then applied to the question of choosing between competing algorithms. ``Leave-one-out'' cross-validation is hence characterised as a crude method of ML-II prior selection. We conclude by examining how the mathematical results bear on practical problems and by discussing related work, as well as suggesting future work.


  551. Alonso, Eduardo. 1995. Negotiation and Social Action in Cooperative Situations. Proceedings of the Fourth International Colloquium on Cognitive Science (ICCS-95),
    ID article: 2514


  552. Frisch, Alan M.. 1995. Feature-Based Grammars as Constraint Grammars. Linguistics and Computation, Ed: J. Cole and G. M. Green and J. L. Morgan, CSLI Publications:85--100, Stanford, CA,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/features.ps.gz ,
    ID article: 2356


  553. Suresh Manandhar. March 1995. Deterministic Consistency Checking of LP Constraints. Seventh Conference of the European Chapter of the Association for Computational Linguistics (EACL'95), Available from the CMP-LG archive http://xxx.soton.ac.uk/archive/ cmp-lg:165-172, Dublin, Ireland,
    http://www.aclweb.org/anthology/E/E95/E95-1023.pdf ,
    ID article: 3171


  554. Anthony D. Griffiths and Derek G. Bridge. 1995. Formalising the Knowledge Content of Case Memory Systems. Progress in Case-Based Reasoning (Procs of 1st UK Workshop 1995), Ed: I.D.Watson, Springer-Verlag,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/UK.ps.gz ,
    ID article: 2667


  555. Duffy, D., MacNish, C., McDermid, J. and Morris, P. 1995. A Framework for Requirements Analysis Using Automated Reasoning. CAiSE*95: Proc. Seventh Advanced Conference on Information Systems Engineering, Ed: Iivari, J. and Lyytinen, K. and Rossi, M., LNCS 932, Springer-Verlag:68--81,
    http://www.cs.york.ac.uk/isg/papers/david.duffy/caise95.ps.gz ,
    ID article: 2508


  556. S. Muggleton. 1994. Bayesian Inductive Logic Programming. Proceedings of the Eleventh International Machine Learning Conference, Ed: W. Cohen and H. Hirsh, Morgan-Kaufmann, Keynote presentation:371--379, San Mateo, CA,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/bayesian.ps.gz ,
    ID article: 2599


  557. Frisch, Alan M. and Haddawy, Peter. September 1994. Anytime Deduction for Probabilistic Logic. Artificial Intelligence, Also appears as Artificial Intelligence Technical Report No. UIUC-BI-AI-92-02, Beckman Institute, Univ. of Illinois at Urbana-Champaign, November, 1992., 69(1):93--102,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/anytime.ps.gz ,
    ID article: 2354


  558. Suresh Manandhar. 1994. An attributive logic of set descriptions and set operations. Proceedings of the 32nd annual meeting on Association for Computational Linguistics, Association for Computational Linguistics:255--262, Morristown, NJ, USA,
    www.ldc.upenn.edu/acl/P/P94/P94-1035.pdf ,
    ID article: 3124


  559. S. Muggleton and L. De Raedt. 1994. Inductive Logic Programming: Theory and Methods. Journal of Logic Programming, 19:629--679,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/lpj.ps.gz ,
    ID article: 2591


  560. James Cussens. 1994. Review of ``Interactive Theory Revision: An Inductive Logic Programming Approach''. Journal of Logic and Computation,
    ID article: 2417


  561. Miles Osborne. 1994. Learning Unification-Based Natural Language Grammars, Available as Technical Report No. YCST95/03, Department of Computer Science, University of York,
    http://www.cs.york.ac.uk/isg/papers/miles.osborne/mothesis.ps.gz ,
    ID article: 2660


  562. S. Muggleton. 1994. Logic and learning: Turing's legacy. Machine Intelligence 13, Ed: K. Furukawa and D. Michie and S. Muggleton, Oxford University Press:37--56,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/tll.ps.gz ,
    ID article: 2594


  563. Olivier, P. and Tsujii, J.. 1994. A Quantitative Perceptual Model of the Semantics of Spatial Prepositions. AI Review, 8(2),
    ID article: 2555


  564. M. Sternberg, J. Hirst, R. Lewis and R. King. 1994. Application of Machine Learning to Protein Structure Prediction and Drug Design. Advances in Molecular Bioinformatics, Ed: S. Schulze-Kremer, IOS Press:1--8,
    ID article: 2596


  565. S. Muggleton. 1994. Bayesian Inductive Logic Programming. Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, Ed: M. Warmuth, ACM Press, Keynote presentation:3--11, New York,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/bayesian.ps.gz ,
    ID article: 2600


  566. Frisch, Alan M. and Page Jr., C. David. April 1994. Wanted: A Theory of Approximation for Automated Reasoning. Working Notes, 1994 AISB Workshop on Automated Reasoning: Bridging the Gap between Theory and Practice, Ed: Alan M. Frisch:24--25, Leeds, Society for the Study of Artificial Intelligence and Simulation of Behavior,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/bridging94.ps.gz ,
    ID article: 2355


  567. A. Srinivasan, S. Muggleton and M. Bain. 1994. The justification of logical theories based on data compression. Machine Intelligence 13, Ed: K. Furukawa and D. Michie and S. Muggleton, Oxford University Press:87--121,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/mi13just.ps.gz ,
    ID article: 2595


  568. Claire Grover, Chris Brew, Suresh Mananhar and Marc Moens. 1994. Priority Union and Generalisation in Discourse Grammars. ACL'94, Available from the CMP-LG archive http://xxx.soton.ac.uk/archive/ cmp-lg:17-24, Las Cruces, New Mexico, Association for Computational Linguistics,
    http://www.cs.york.ac.uk/isg/papers/suresh.manandhar/priority.ps.gz ,
    ID article: 2471


  569. Craig MacNish. 1994. A Practical Theory of Nonmonotonic Temporal Modelling. Proc. AAAI-94 Workshop on Spatial and Temporal Reasoning, Ed: F. D. Anger and R. Loganantharaj and R. Rodríguez:63-68, Seattle, Washington,
    http://www.cs.york.ac.uk/isg/papers/craig.macnish/aaai94.ps.gz ,
    ID article: 2655


  570. Miles Osborne and Derek G. Bridge. 1994. Learning Unification-Based Grammars Using the Spoken English Corpus. Grammatical Inference and Applications: Second International Colloquium on Grammatical Inference, Ed: R. C. Carrasco and J. Oncin, Springer-Verlag:260--270,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/spain.ps.gz ,
    ID article: 2658


  571. S. Muggleton. 1994. Predicate Invention and Utilisation. Journal of Experimental and Theoretical Artificial Intelligence, Taylor & Francis, 6(1):127--130,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/util.ps.gz ,
    ID article: 2592


  572. Bryant, C. H., Adam, A. E., Taylor, D. R. and Rowe, R.C.. 1994. Review of Expert Systems for Chromatography.. Analytica Chimica Acta, 297(3):317--347,
    ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_aca_review.ps.gz ,
    ID article: 2495.

    Abstract:
    Expert systems for chromatography are reviewed. A taxonomy is proposed that allows present (and future) expert systems in this area to be classified and facilitates an understanding of their inter-relationship. All the systems are described focusing on the reasons for their development, what their purpose was and how they were to be used. The engineering methods, knowledge representations, tools and architectures used for the systems are compared and contrasted in a discussion covering all the stages of the development life cycle of expert systems. The review reveals that too often developers of expert systems for chromatography do not justify their decisions on engineering matters and that the literature suggests that many ideas advocated by knowledge engineers are not being used.


  573. S. Muggleton and C.D. Page. 1994. Self-saturation of definite clauses. Proceedings of the Fourth International Inductive Logic Programming Workshop, Ed: S. Wrobel, Gesellschaft fur Mathematik und Datenverarbeitung MBH, GMD-Studien Nr 237:161--174,
    ID article: 2597


  574. S. Muggleton and C.D. Page. 1994. A Learnability Model for Universal Representations, ftp://ftp.cs.york.ac.uk/pub/ML\_GROUP/Papers/ulearn10.ps.gz, Oxford, Oxford University Computing Laboratory,
    ID article: 2601


  575. A. Srinivasan, S. Muggleton, R. King and M. Sternberg. 1994. Mutagenesis: ILP experiments in a non-determinate biological domain. Proceedings of the Fourth International Inductive Logic Programming Workshop, Ed: S. Wrobel, Gesellschaft fur Mathematik und Datenverarbeitung MBH, GMD-Studien Nr 237,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ilp94.ps.gz ,
    ID article: 2598


  576. Claire Grover, Chris Brew, Suresh Manandhar and Marc Moens. 1994. Priority Union and Generalisation in Discourse Grammars. In 32nd Annual Meeting of the Association for Computational Linguistics (ACL):255 - 262, Las Cruces, New Mexico,
    http://www-users.cs.york.ac.uk/~suresh/papers/PUAGIDG.pdf ,
    ID article: 2925


  577. K. Furukawa, D. Michie and S. Muggleton. 1994. Machine Intelligence 13: machine intelligence and inductive learning, Oxford University Press, Oxford,
    ID article: 2549


  578. Miles Osborne and Derek G. Bridge. 1994. More for Less: Learning a Wide Covering Grammar from a Small Training Set. Proc. of the First International Conference on New Methods in Natural Language Processing (NemLap-94):168--173, Manchester, England,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/umist.ps.gz ,
    ID article: 2659


  579. S. Muggleton. 1994. Inductive Logic Programming: derivations, successes and shortcomings. SIGART Bulletin, 5(1):5-11,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/sigart.ps.gz ,
    ID article: 2593


  580. M. Sternberg, R. King, R. Lewis and S. Muggleton. 1994. Application of Machine Learning to Structural Molecular Biology. Philosophical Transactions of the Royal Society B, 344:365--371,
    ID article: 2590


  581. Suresh Manandhar. August 1993. CUF in Context. Computational Aspects of Constraint-Based Linguistic Description I, Ed: Jochen D, DYANA-2, Deliverable R1.2.A, Available from ftp://illc-sun.illc.uva.nl/pub/dyana/R1.2.A,
    http://www-users.cs.york.ac.uk/~suresh/papers/CIC.ps.gz ,
    ID article: 2926


  582. Dimitar Kazakov. 1993. Modul pro komunikaci v prirozeném jazyce, Prague, Czech Republic, Czech Technical University,
    ID article: 2376


  583. Muggleton, S.. 1993. Optimal layered learning: A PAC approach to incremental sampling. Proceedings of the 4th Conference on Algorithmic Learning Theory, Ed: K. Jantke and S. Kobayashi and E. Tomita and T. Yokomori, Springer-Verlag, LNAI 744:37-44,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/layered.ps.gz ,
    ID article: 2589


  584. C. Brew, J. Dorrepaal, C. Gardent and C. Grover. 1993. Representation of Discourse Information, Research Report, LRE 61-062 [B.1], Human Communication Research Centre, University of Edinburgh,
    http://www-users.cs.york.ac.uk/~suresh/papers/RODI.pdf ,
    ID article: 2927


  585. Miles Osborne and Derek G. Bridge. 1993. Inductive and Deductive Grammar Learning: Dealing with Incomplete Theories. Proc. of the First IEE Colloquium on Grammatical Inference: Theory, Applications and Alternatives, Essex,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/essex.ps.gz ,
    ID article: 2657


  586. James Cussens. 1993. Bayes and pseudo-Bayes estimates of conditional probability and their reliability. Machine Learning: ECML-93, Ed: Pavel B. Brazdil, Lecture Notes in Artificial Intelligence 667, Springer-Verlag:136--152,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ecml93.ps.gz ,
    ID article: 2414.

    Abstract:
    Various ways of estimating probabilities, mainly within the Bayesian framework, are discussed. Their relevance and application to machine learning is given, and their relative performance empirically evaluated. A method of accounting for noisy data is given and also applied. The reliability of estimates is measured by a significance measure, which is also empirically tested. We briefly discuss the use of likelihood ratio as a significance measure.


  587. Alonso, Eduardo and MarroquĂ­n, J. M.. 1993. Emotion and Planning. Proceedings of the Third International Colloquium on Cognitive Science (ICCS-93),
    ID article: 2513


  588. James Cussens, Anthony Hunter and Ashwin Srinivasan. 1993. Generating explicit orderings for non-monotonic logics. Proc. of the Eleventh National Conference on Artificial Intelligence (AAAI-93), MIT Press:420--425,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/aaai.ps.gz ,
    ID article: 2415


  589. Charlene Bloch Abrams and Alan M. Frisch. March 1993. An Examination of the Efficiency of Sorted Deduction, Artificial Intelligence Technical Report, Beckman Institute, University of Illinois at Urbana-Champaign,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/examination.ps.gz ,
    ID article: 2353


  590. Suresh Manandhar. 1993. Relational Extensions to Feature Logic: Applications to Constraint Based Grammars, PhD Thesis, Department of Artificial Intelligence, University of Edinburgh,
    http://www-users.cs.york.ac.uk/~suresh/papers/RETFLATCBG.pdf ,
    ID article: 3044


  591. Miles Osborne and Derek G. Bridge. 1993. Learning Unification-Based Grammars and the Treatment of Undergeneration. ECML-93 Workshop on Machine Learning Techniques and Text Analysis, Vienna,
    http://www.cs.york.ac.uk/isg/papers/derek.bridge/vienna.ps.gz ,
    ID article: 2656


  592. Andrews, N. and Brown, J. C.. 1993. A High-Speed Natural-Language Parser. AISB Quarterly:12-19,
    ID article: 2544


  593. Ed: S. Muggleton. 1993. Proceedings of the Third International Workshop on Inductive Logic Programming, Ed: S. Muggleton, Jozef Stefan Institute, Bled, Slovenia,
    ID article: 2588


  594. James Cussens and Anthony Hunter. 1993. Using maximum entropy in a defeasible logic with probabilistic semantics. IPMU'92 - Advanced Techniques in Artificial Intelligence, Ed: B. Bouchon-Meunier and L. Valverde and R.R. Yager, Lecture Notes in Computer Science 682, Springer-Verlag:43--52,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ipmu92.ps.gz ,
    ID article: 2416


  595. A. Srinivasan, S. Muggleton and M. Bain. 1992. Distinguishing exceptions from noise in non-monotonic learning. Proceedings of the Second Inductive Logic Programming Workshop, ICOT (Technical report TM-1182):97--107, Tokyo,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/new_nil.ps.gz ,
    ID article: 2582


  596. MacNish, Craig. 1992. Knowledge Without Modality: A Simplified Framework for Chronological Ignorance. Logics in AI: Proc. European Workshop JELIA'92, Ed: Pearce, D and Wagner, G., LNAI--633, Springer-Verlag:25--35, Berlin, Germany,
    http://www.cs.york.ac.uk/isg/papers/craig.macnish/jelia92.ps.gz ,
    ID article: 2654


  597. S. Muggleton, A. Srinivasan and M. Bain. 1992. Compression, significance and accuracy. Proceedings of the Ninth International Machine Learning Conference, Ed: D. Sleeman and P. Edwards, Morgan-Kaufmann:338--347, San Mateo, CA,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/compsig.ps.gz ,
    ID article: 2585


  598. Ed: S. Muggleton. 1992. Inductive Logic Programming, Ed: S. Muggleton, Academic Press,
    ID article: 2579


  599. S. Dzeroski, S. Muggleton and S. Russell. 1992. PAC-learnability of determinate logic programs. Proceedings of the 5th ACM Workshop on Computational Learning Theory, Pittsburg, PA,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/colt92.ps.gz ,
    ID article: 2583


  600. C. David Page Jr. and Alan M. Frisch. 1992. Generalization and Learnability: A Study of Constrained Atoms. Inductive Logic Programming, Ed: Stephen H. Muggleton, Academic Press, chapter: 2:29-61, London,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/generalization.ps.gz ,
    ID article: 2352


  601. C. Feng and S. Muggleton. 1992. Towards inductive generalisation in higher order logic. Proceedings of the Ninth International Workshop on Machine Learning, Ed: D. Sleeman and P. Edwards, Morgan Kaufmann:154--162, San Mateo, CA,
    ID article: 2584


  602. Suresh Manandhar. 1992. A logic for set descriptions. In ESSLLI Workshop on Theoretical Foundations of Feature Logic, European Summer School in Logic, Language and Information(ESSLI), Lisbon, Portugal,
    http://www-users.cs.york.ac.uk/~suresh/papers/ALFSD.pdf ,
    ID article: 2929


  603. S. Muggleton. 1992. Inverting Implication. Proceedings of the Second Inductive Logic Programming Workshop, ICOT (Technical report TM-1182):19--39, Tokyo,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/invimp.ps.gz ,
    ID article: 2586


  604. Ed: S. Muggleton. 1992. Proceedings of the Second International Workshop on Inductive Logic Programming, Ed: S. Muggleton, ICOT, Tokyo, Japan,
    ID article: 2580


  605. M. Sternberg, R. Lewis, R. King and S. Muggleton. 1992. Modelling the structure and function of enzymes by machine learning. Proceedings of the Royal Society of Chemistry: Faraday Discussions, 93:269--280,
    ID article: 2578


  606. B. Dolsak and S. Muggleton. 1992. The application of Inductive Logic Programming to finite element mesh design. Inductive Logic Programming, Ed: S. Muggleton, Academic Press:453--472, London,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/fem1.ps.gz ,
    ID article: 2581


  607. S. Muggleton, R. King and M. Sternberg. 1992. Protein secondary structure prediction using logic-based machine learning. Protein Engineering, 5(7):647--657,
    ID article: 2577


  608. S. Muggleton. 1992. Developments in Inductive Logic Programming. Proceedings of the International Conference on Fifth Generation Computer Systems 1992, Ohmsha:1071--1073, Tokyo,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/fgcs.ps.gz ,
    ID article: 2587


  609. R. King, S. Muggleton, R. Lewis and M. Sternberg. 1992. Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proceedings of the National Academy of Sciences, 89(23):11322--11326,
    ID article: 2576


  610. James Cussens. 1992. Estimating Rule Accuracies from Training Data. Logical Approaches to Machine Learning, ECAI-92 Workshop Notes,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ilp92.ps.gz ,
    ID article: 2413


  611. Alan M. Frisch and Richard B. Scherl. 1991. A General Framework for Modal Deduction. Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference, Ed: James Allen and Richard Fikes and Erik Sandewall, Morgan Kaufman:196-207, San Mateo, CA,
    http://www.cs.york.ac.uk/isg/papers/alan.frisch/modal.ps.gz ,
    ID article: 2351


  612. James Cussens. 1991. Interpretations of Probability, Nonstandard Analysis and Confirmation Theory, King's College, London,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/jcths.ps.gz ,
    ID article: 2411.

    Abstract:
    The first chapter presents Bayesian confirmation theory. We then construct infinitesimal/ numbers and use them to represent the probability of unrefuted hypotheses of standard probability zero. Popper's views on the nature of hypotheses, of probability and confirmation are criticised. It is shown that Popper conflates total confirmation/ with weight of evidence. It is argued that Popper's corroboration/ can be represented in a Bayesian formalism. Popper's propensity theory is discussed. A modified propensity interpretation is presented where probabilities are defined relative to descriptions of generating conditions. The logical interpretation is briefly discussed and rejected. A Bayesian account of estimating the values of objective probabilities is given, and some of its properties are proved. Belief functions/ are then compared with probabilities. It is concluded that belief functions offer a more elegant representation of the impact of evidence. Both measures are then discussed in relation to various betting procedures/ designed to elicit their values from an individual's belief state. De Finetti's arguments concerning `coherence' are discussed. It is then shown that it is not possible to use bets to derive belief function values unless the better is allowed to vary the amount of the stake. Hume's thinking on induction is discussed. It is argued that some of the problems of Popper's philosophy derive from Hume's. The Popper-Miller argument/ is presented and criticised. It is concluded that the core of the argument is valid, but of limited applicability. The correspondence between probabilistic support and deductive relations is discussed. There are two appendices. The first criticises Popper's view on the connection between the content and testability of a hypothesis. The second concerns a nonstandard probability measure


  613. S. Muggleton. 1991. Inverting the resolution principle. Machine Intellience 12, Oxford University Press:93--104,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/mi12.ps.gz ,
    ID article: 2575


  614. James Cussens and Anthony Hunter. 1991. Using defeasible logic for a window on a probabilistic database: some preliminary notes. Symbolic and Quantitative Approaches for Uncertainty, Ed: R. Kruse and P. Seigel, Lecture Notes in Computer Science 548, Springer-Verlag:146--152,
    ID article: 2412


  615. I. Bratko, S. Muggleton and A. Varsek. 1991. Learning Qualitative Models of Dynamic Systems. Proceedings of the Eighth International Machine Learning Workshop, Morgan-Kaufmann, San Mateo, Ca,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ewsl91.ps.gz ,
    ID article: 2574


  616. Ed: S. Muggleton. 1991. Proceedings of the First International Workshop on Inductive Logic Programming, Ed: S. Muggleton, University of Porto, Porto, Portugal,
    ID article: 2573


  617. S. Muggleton. 1991. Inductive Logic Programming. New Generation Computing, 8(4):295--318,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/ilp.ps.gz ,
    ID article: 2572


  618. M. Bain and S. Muggleton. 1991. Non-monotonic Learning. Machine Intelligence 12, Ed: D. Michie, Oxford University Press:105--120,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/nonmon.ps.gz ,
    ID article: 2552


  619. S. Muggleton. 1990. Inductive Acquisition of Expert Knowledge, Addision-Wesley, Wokingham, England,
    ID article: 2570


  620. S. Muggleton and C. Feng. 1990. Efficient induction of logic programs. Proceedings of the First Conference on Algorithmic Learning Theory, Ohmsha, Tokyo,
    ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/alt90.ps.gz ,
    ID article: 2571


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