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Non-Standard Computation (NSC)

We research reality-based computing approaches that seek their inspiration from the natural world (biology, chemistry, physics). Close work is undertaken with mathematicians and physicists on aspects of quantum computation and quantum cryptology. As part of the York Centre for Complex Systems Analysis, the group work with engineers and biologists on complex emergent systems and novel bio-inspired computational paradigms.

Group Aims

We explore new computational paradigms that break the classical computational assumptions. The real world has already provided the inspiration for: novel algorithms, including genetic algorithms, swarm algorithms, and artificial immune systems; novel views of what constitutes a computation, such as complex adaptive systems, and self-organising networks; novel computational foundations, such as quantum computing. The Group is participating in the quest to produce a fully mature science of all forms of computation, that unifies the classical and non-classical paradigms.

Senior Member: Susan Stepney

Contact Person: Susan Stepney

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Research Areas

Quantum Computing presents one of the most exciting developments for computer science in recent times. Based on quantum physics, it can perform computations that cannot be effectively implemented on a classical Turing machine. It exploits interference, many worlds, entanglement and non-locality. Newer work still is further breaking out of the binary mind-set, with multiple-valued "qubits", and continuous variables. The subject covers computation, information theory, and communication protocols. The group works closely with other interested Departments in York, by running the Quantum Computing SIG.

Reality-based Computation has inspired many important techniques in computer science, drawing inspiration from physics (simulated annealing), evolution (genetic algorithms, genetic programming), neurology (artificial neural networks), immunology (artificial immune systems), plant and animal growth (L-systems), social networks (ant colony optimisation), and others.

In the virtual worlds inside the computer, we are no longer constrained by the laws of nature, and can go beyond the precise way the real world works. For example, we can introduce novel evolutionary operators to our genetic algorithms, novel kinds of neurons to our neural nets, and even, as we come to understand the embracing concepts, novel kinds of complex adaptive systems themselves.

Post-classical Refinement is needed, to permit quantitative reasoning about all reality-inspired algorithms. We need to understand and predict the global properties that emerge from a collection of local non-specific agents, so that we can we design (refine) systems that have desired emergent properties, and do not have undesired emergent properties. We need to be able to design and implement appropriate algorithms for particular applications, in a rigorous (but possibly non-incremental) way, and to give quantitative description methods that enable rigorous reasoning about the behaviour of the algorithms, such that they can be used reliably in critical applications.

Massive Parallelism, as seen in cellular automata, and agent systems, and as realised in FPGAs, is needed to effectively implement many of these reality-based algorithms.

Computational Trajectories, measured as a program is executing, are a computational resource in their own right. Logical trajectories, tracing the path through the logical state space, and physical trajectories, measuring physical changes during execution, are both valuable.

Dynamic Reaction networks can exhibit the emergent complexity, complex dynamics, and self-organising properties of many far-from-equilibrium systems. These systems, and others, can self-organise into regions 'at the edge of chaos', neither too ordered nor too random, where they can perform interesting computations. There are many dynamic network models that occur in biological and social systems: autocatalytic networks, genomic control networks, dynamical neural networks and cytokine immune networks, ecological food webs, etc. Realistic models of such networks need a pragmatic theory of dynamic, heterogeneous, unstructured, open networks.

Open Dynamical Systems includes the full consideration of computation as a dynamical process, computation at the edge of chaos, including its fundamental capabilities, and designed emergence. We need to know the fundamental properties of such systems. We need to understand the events that can open up new kinds of regions of phase space to a computation. And we want to design, and predict the effect of, interventions (adding new things, or removing things) to the system.

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  • EU
  • NERC
  • Microsoft Research Europe
  • NCR

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Research Projects

  • Material Computation with Structure and Dynamics: bulk NMR feasibility study
  • The Birth, Life and Death of Semantic Mutants
  • PAB: Plasticity and robustness in the Arabidopsis shoot branching regulatory network
  • SABRE: Self-healing Cellular Architectures for Biologically-inspired Highly Reliable Electronic Systems
  • Albino: Artificial Biochemical Networks: Computational Models and Architectures
  • PLAZZMID: Evolutionary algorithms from bacterial and bee genomes
  • SYMBRION: Symbiotic Evolutionary Robot Organisms
  • TRANSIT: TRANSition from Interdisciplinarity to Transdiciplinarity
  • Building Capacity in Complex Systems and Complexity Theory
  • Quantum Computation: Foundations, Security, Cryptography and Group Theory
  • CoSMoS: Complex Systems Modelling and Simulation
  • Using Learning to Support the Development of Embedded Systems
  • DAMSONS: Developing a Methodology for Social Network Sampling
  • System-Smart Intrusion Detection
  • Foundational Structures for Quantum Information and Computation (QICS)
  • Defending the Weakest Link: Intrusion via Social Engineering
  • SEBASE: Software Engineering By Automated SEarch
  • Exploiting Immunological Principles for Increased Integrity in ATMs
  • Journeys in Non-Classical Computation
  • EvoEvo
  • CellBranch
  • Interspecific Information Transfer as a Driver of Community Structure in Savanna Herbivores (NERC grant)
  • The Evolution of Post-Reproductive Lifespan in a Non-Human Mammal (NERC grant)

Browse through our list of our current research projects for further information.

A list of past research projects is available here.

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Academic Members of the Group

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Non-Standard Computation (NSC)

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Researcher Profile

Jennifer Owen

Jennifer Owen

"I am working on making the process of evolving a working swarm behaviour faster."

Research area: Evolutionary Swarm Robotics

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