Areas

Data Science

Chris Huyck, David Windridge, Ian Mitchell, Roman Belavkin, Xiaohong Gao.

Many of our projects involve anlaysis of data using various data-mining and machine learning algrithms. We share some of the datasets here.

Neural Nets, Pattern Recognition and Categorisation

Chris Huyck, David Windridge, Ian Mitchell, Roman Belavkin, Xiaohong Gao.

We work on a wide range of neural networks including traditional feed-forward networks, self-organising maps, growing cell structures and recurrent networks. We also use statistical techniques, such as independent component analysis and vector support machines. We try to apply these systems to the problems appropriate for them including sequences, concept representation, categorisation, learning financial data and text mining. We are hoping to concentrate some effort on categorisation. Many of us have different approaches to categorisation including different types of neural networks, symbolic classification and statistical methods. We are hoping to combine this research to find fundamental concepts in categorisation.

Natural Language Processing

Chris Huyck, Gill Whitney.

This group works in many areas of NLP including parsing, word sense disambiguation, use of language in planning, cross linguistic issues, text extraction, story production, text summarisation, and dialogue agents.

Cognitive Modelling and Computational Learning

Roman Belavkin, Christian Huyck

We are interested in understanding intelligence by studying and learning from biological intelligent systems. In particular, we are using the cognitive modelling approach, where theories of learning and behaviour are tested in computer simulations. We are experts in cognitive architectures, such as Soar and ACT-R, and actively participate in the international cognitive science and cognitive modelling community.

We are also interested in the fundamental nature of connectionist systems and biological neural functioning. We experiment with large neural networks based on models of biological neurons and cell assemblies. Some of the problems we are working on include symbol grounding, variable binding and symbolic processing in connectionist systems.

Optimisation and Machine Learning

Christian Huyck, Ian Mitchell, David Windridge, Roman Belavkin.

We are interested in developing and applying general optimisation algorithms, such as genetic algorithms and stochastic simulations. We are interested in applying these methods for problems of optimal control and planning, investigating the effectiveness of finding optimal solutions to hard problems, such as maximal clique and the travelling salesman problem. Coming up with a metric to measure the effectiveness of a variant GA often depends on the problem domain. However, if a representation that is independent of GAs is possible, then this representation can be used as a test bed for future variants of the problem. Modelling aides the business procedures by identifying; where subprocesses are repeated through an organisation and where bottlenecks and redundancies occur, thus enabling organisations to reach their objectives more efficiently.