Publications:
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Dimitar Kazakov and Tsvetomira Tsenova. January 2009. Equation Discovery for Macroeconomic Modelling. International Conference on Agents and Artificial Intelligence, Porto, Portugal,
ID article: 3072
- 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.
- 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
- 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
- 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
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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.
- R. Alfred. 2007. The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining. ADMA:214-226,
ID article: 2711
- 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
- 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.
- 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
- 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
- R. Alfred and D. Kazakov. 2007. Clustering Approach to Generalised Pattern Identification Based on Multi-Instanced Objects with DARA. ADBIS,
ID article: 2714
- 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
- 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
- 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
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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
- R. Alfred and D. Kazakov. 2007. Discretisation Numbers for Multiple-Instances Problem in Relational Database. ADBIS:55-65,
ID article: 2712
- 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
- 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.
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- James Cussens. 2007. Bayesian classification and regression trees. Expert Update, 9(3):37--42,
ID article: 2680
- 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.
- R. Alfred and D. Kazakov. 2007. Aggregating Multiple Instances in Relational Database Using Semi-Supervised Genetic Algorithm-Based Clustering Technique. ADBIS,
ID article: 2713
- 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
- D. Kazakov.. November 2006. Extended fitness for evolutionary algorithms.. META'06 Workshop on Metaheuristics.,
ID article: 2256
- 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.
- 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
- 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
- Z. Lock and D. Kudenko. 2006. Interactions between Stereotypes. Adaptive Hypermedia and Adaptive Web-Based Systems (AH ’06).,
ID article: 2275
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- D. Kazakov.. November 2006. Self-reflective Machine Learning Challenge.. META'06 Workshop on Metaheuristics.,
ID article: 2257
- 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
- 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
- Björn Þór Jónsson, Maria Arinbjarnar, Bjarnsteinn Þórsson 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
- 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.
- 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
- 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
- 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
- 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: LuÃs 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.
- 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
- 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
- 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
- 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
- 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
- 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
- R. Alfred and D. Kazakov. 2006. Weighted Pattern-Based Transformation Approach to Relational Data Mining. ICAIET,
ID article: 2717
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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.
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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.
- 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
- 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
- Caron, Vincent and Andrews, Pierre. 2005. SPIP, mémento, Eyrolles, Paris,
http://www.eyrolles.com/Informatique/Livre/9782212117325/livre-memento-spip.php ,
ID article: 2308
- 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.
- 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
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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.
- 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
- 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
- Alonso, Eduardo. 2004. Rights and Argumentation in Open Multi-Agent Systems. Artificial Intelligence Review, 21,
ID article: 2534
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- Yuan, T.. 2004. Human Computer Debate, a Computational Dialectics Approach, Ph.D. Thesis - Computer Science, Leeds Metropolitan University,
ID article: 3039
- 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
- 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
- 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
- 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
- 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
- James Cussens. December 2004. Integrating by Separating: Combining Probability and Logic with ICL, PRISM and SLPs, APRIL project report,
ID article: 2323
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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.
- 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.
- 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
- 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
- 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.
- 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
- 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
- M. De Boni M. and M. Prigmore. 2003. Privacy and the Information Economy. Proceedings of the IADIS Internations e-Society,
ID article: 2339
- 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
- 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
- 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
- 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
- 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
- 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
- Miguel, I. and Shen, Q.. 2003. Fuzzy rrDFCSP and Planning. Artificial Intelligence, 148(1):11-52,
ID article: 2408
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- T. Walsh. 2001. Search on High Degree Graphs. Proceedings of 17th IJCAI, IJCAI,
Yorkcategory: D - Refereed International Conference Paper,
ID article: 2492
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- M. De Boni. 2001. A Study on the Centrality of Relevance for Automated Question Answering, University of York,
ID article: 2347
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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.
- 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
- 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.
- 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
- 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.
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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.
- 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.
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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.
- Alonso, Eduardo. 1999. Inteligencia Artificial Distribuida: cómo entederla y usarla. Divulgación Científica,
http://www.arrakis.es/~jjreina/revista/articulo/iad ,
ID article: 2525
- 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
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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
- 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.
- 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.
- 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
- Patrick Olivier. 1998. Kinematic Reasoning with Spatial Decompositions. Constraints,
ID article: 2562
- 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
- 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
- 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
- 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
- Dimitar Kazakov, Steve Pulman and Stephen Muggleton. 1998. The FraCaS dataset and the LLL challenge, Unpublished report,
ID article: 2386
- 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
- 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.
- 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
- 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
- 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.
- 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.
- 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
- 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
- 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
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Patrick Olivier. 1997. Hierarchy and Attention in Computational Imagery. Machine Graphics and Vision, 6(1):77-88,
ID article: 2559
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- Patrick Olivier. 1997. Estimating Visuospatial Properties in Graphics. British Computer Society SGES Expert Systems Conference, Cambridge, UK,
ID article: 2560
- 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
- Alonso, Eduardo. 1997. A Formal Framework for the Representation of Negotiation Protocols. Inteligencia Artificial:30--49,
ID article: 2517
- 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
- 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
- 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\%.
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Patrick Olivier. 1997. Co-ordinating the Visual and Verbal Domains. ACM Workshop on Perceptual User Interfaces, Banff, Alberta, Canada,
ID article: 2561
- James Cussens. August 1996. Machine Learning. IEE Journal of Computing and Control, 7(4):164--168,
ID article: 2420
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- Dimitar Kazakov. 1996. Inductive Learning of LR parsers from treebanks, ISSN 0751-1345, ENST, Paris,
ID article: 2380
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- Libor Jelínek and Dimitar Kazakov. 1996. Man-robot natural language interaction, The Gerstner Laboratory for Intelligent Decision Making,
Czech Technical Universit,
ID article: 2379
- 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
- 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
- Alonso, Eduardo. 1996. Agentes Locales y Autónomos en Inteligencia Artificial
Distribuida: Coordinación, UPV-EHU,
ID article: 2515
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- Brown, John C.. December 1995. High Speed Feature Unification and Parsing. Natural Language Engineering:309-338,
ID article: 2545
- 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
- K. Furukawa, D. Michie and S. Muggleton. 1995. Machine Intelligence 14: machine intelligence and inductive
learning, Oxford University Press, Oxford,
ID article: 2550
- 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
- Alonso, Eduardo. 1995. Negotiation and Social Action in Cooperative Situations. Proceedings of the Fourth International Colloquium on Cognitive
Science (ICCS-95),
ID article: 2514
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- K. Furukawa, D. Michie and S. Muggleton. 1994. Machine Intelligence 13: machine intelligence and inductive
learning, Oxford University Press, Oxford,
ID article: 2549
- 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
- Olivier, P. and Tsujii, J.. 1994. A Quantitative Perceptual Model of the Semantics of Spatial
Prepositions. AI Review, 8(2),
ID article: 2555
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- James Cussens. 1994. Review of ``Interactive Theory Revision: An Inductive
Logic Programming Approach''. Journal of Logic and Computation,
ID article: 2417
- 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
- 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
- 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
- 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
- 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
- Andrews, N. and Brown, J. C.. 1993. A High-Speed Natural-Language Parser. AISB Quarterly:12-19,
ID article: 2544
- 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
- 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
- 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
- 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
- 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
- Dimitar Kazakov. 1993. Modul pro komunikaci v prirozeném jazyce, Prague, Czech Republic, Czech Technical University,
ID article: 2376
- 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
- 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.
- 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
- Alonso, Eduardo and MarroquÃn, J. M.. 1993. Emotion and Planning. Proceedings of the Third International Colloquium on Cognitive
Science (ICCS-93),
ID article: 2513
- 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
- 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
- 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
- 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
- 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
- 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
- Ed: S. Muggleton. 1992. Inductive Logic Programming, Ed: S. Muggleton, Academic Press,
ID article: 2579
- 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
- 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
- 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
- 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
- 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
- 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
- Ed: S. Muggleton. 1992. Proceedings of the Second International Workshop on Inductive Logic
Programming, Ed: S. Muggleton, ICOT, Tokyo, Japan,
ID article: 2580
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- S. Muggleton. 1991. Inverting the resolution principle. Machine Intellience 12, Oxford University Press:93--104,
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