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AI Group

School of Computing Science

Middlesex University

Artificial Intelligence

The Artificial Intelligence and Neural Nets research group is a collection of researchers interested in a wide area of AI and Neural Net specialities. Our range of backgrounds widens our expertise and enables us to discuss a variety of AI problems. Areas of expertise in the group include: Natural Language Processing, Distributed Memory, Planning and Self-Organising Maps. The areas of skills range over Genetic Algorithms, Expert Systems, Logic, feed-forward Neural Networks, and Computational Neurosciences.


  1. Mind and Brain Architecture:
  2. NL2Enact: by Geetha Abeysinghe and Christian Huyck
    This is an EPSRC funded project which aims to develop a Natural Language Processing system, which can convert a business process description in free text to an executable formal notation. The graphical models of the business process produced by the execution will then be used for verification and further elicitation of the business process. The work on the project will begin in late July/early August.
  3. Cognitive Systems by Roman Belavkin
    Cognitive Systems is a general group involving:
    multidisciplinary collaboration on building cognitive systems.
  4. Cross Language Information Retrieval by Viviane Orengo and Chris Huyck
    Digitized text is stored in many languages. When searching for information, the user may need a document that is in a language in which he is not fluent. Multi-lingual thesauri and large multi-lingual corpora can be used to develop tools that can enable a user to search documents in languages in which he is not fluent.
  5. Traveling Salesman and GAs by Ian Mitchell
    The technique, essentially, relies on each node on the TSP graph sending messages to all possible other nodes in parallel, such a technique exploits the collision of messages. A genetic algorithm is introduced to optimise the search space. The use of temporal representations used to solve the TSP has no illegal representations encoded in the gene and hence repair algorithms are unnecessary. This paper investigates the exploitation of message collision, the post collision process and how certain sequences of events yield a near optimal solution to the TSP.
  6. Hebbian Cell Assemblies by Chris Huyck
    The CANT (Connections, Associations and Network Technology) model is designed to function like a natural neural system. The basic idea is derived from Hebb's idea of the Cell Assembly, a reverberating circuit of neurons which is the neural equivalent of a concept. The long-term goal of the model is to discover how CAs work; discover what CAs can do; duplicate psychological data with CAs.
  7. Sequence Recognition using Neural Nets by Ian Mitchell and Siri Bavan
    Given a non-orthogonal training set, recall a sequence from an ambiguous stimulus. This usually results in a winner-takes-all approach, whereby each solution competes until the strongest signal wins. The winner is proposed as the final solution, however how correct it is depends on its use. Information retrieval rarely results in a single output and therefore emphasis is being placed on techniques capable of retrieving multiple memories. At present many retrieval methods rank their results, however many sequences can have equivalent ranking i.e. they are all candidate solutions. It is this problem domain that this project investigates.


  1. Natural Language Processing: Chris Huyck, Geetha Abeysinghe, John Platts, Gill Whitney and Viviane Orengo.
    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 summarization, and dialogue agents. The primary work in this area is currently being done by Geetha, John, Le Than, Keh Kok, and Viviane.
  2. Neural Nets: Chris Huyck, Ian Mitchell, Siri Bavan and Usama Hasan. We work on a wide range of neural networks including traditional feed forward networks, self-organising maps, growing cell structures and recurrent networks. We try to apply these systems to the problems appropriate for them including sequences, concept representation, categorisation and learning financial data. We are also interested in the fundamental nature of connectionist systems and biological neural functioning. The primary work in this area is currently being done by Ian and Chris.
  3. Planning: Geetha Abeysinghe, Christian Huyck, and Roger Witts.
    Planning includes activities which support better procedures in diagnosing, monitoring, and goal seeking. 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. We try to apply Natural Language Processing techniques to process descriptions.
  4. Describing Mathematics Alexei Vernitski.
    Computers have no difficulties in handling mathematics at mechanical level of logic whereas humans generally work at a much higher and generally less precise cognitive level. The rigour of the computer is desirable but without the tedium of lengthy symbolic logic. We are looking to achieve good human interfaces to mathematics on computers by providing methods of rigorously translating between human and computer compatible representations. There is also the issue of how a human might navigate to the appropriate level of logical detail or even what different "levels of logical detail" might mean.
  5. Genetic Algorithms: Ian Mitchell and Paul Cairns
    Investigating the effectiveness of finding optimal solutions to hard problems, such as maximal clique and the traveling salesman problem using Genetic Algorithms, GAs. We are currently investigating how to measure the effectivenes of finding the solution. 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.
  6. Categorisation: all.
    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. Currently, this work is in its infancy, and we are trying to find good metrics for measuring quality of categorisation.
  7. Text Mining: all.
    In the 2001 academic year we are hoping to combine our interests in categorisation and Natural Language Processing and work on Text Mining.


  1. The group meets monthly.
  2. The AI reading group has been meeting for almost 5 years.
  3. The AI group does most of the organisation for Middlesex's BIS seminar
Middlesex Research Group's Page

Email: geetha1@mdx.ac.uk