Wednesday 27th March 2002 (6 pm)
ABSTRACT
Since 1987, I have been developing a conceptual framework and computer models intended to integrate concepts
in cognitive science, artificial intelligence and other aspects of information processing in natural and artificial
systems. This talk will present an overview of the framework and its range of applications, focussing mainly on
the way it may be applied in AI and computing. Examples will be illustrated with output from the computer models.
The talk will not merely repeat my article about these ideas in the Autumn number of Expert Update. Different examples
will be presented and there will be opportunities for questions and discussion.
The conceptual framework -- "information compression by multiple alignment, unification and search" (ICMAUS)
-- originated from the long-established idea that 'economy', 'parsimony' or 'simplicity' has an important role
in human perception and cognition. More immediately, the framework is founded on principles of Minimum Length Encoding
pioneered by Solomonoff, Wallace and Boulton, Rissanen and others. The framework provides an interpretation for
established models of computing such as the Universal Turing Machine and the Post Canonical System. It also provides
an interpretation for a range of concepts in mathematics and logic.
The system may be used to parse sentences syntactically and produce them. Discontinuous dependencies in syntax
can be marked in a simple and direct manner. The system has been designed to facilitate the integration of syntax
and semantics but this area has not yet received close attention. The system is able to recognise patterns despite
errors of omission, commission or substitution. It also supports the recognition of patterns at multiple levels
of abstraction in a hierarchy of classes, with inheritance of attributes from higher levels to lower. The system
may function as a knowledge base with facilities for best-match information retrieval and indirection in information
retrieval.
Various kinds of probabilistic reasoning may be modelled in the system including probabilistic 'deduction' and
abduction, chains of reasoning (and other forms of composite reasoning), nonmonotonic reasoning, 'explaining away',
and solving geometric analogy problems. Given appropriate input, the most recent computer model can achieve unsupervised
learning of simple grammars. The system has potential for knowledge discovery and as a self-organising knowledge
base. More work is needed on these aspects of the framework.
Up until 1983 GERRY WOLFF was a lecturer in psychology at the University of Dundee working on computer models of language learning. Following a one-year research fellowship with IBM in Winchester, he worked for four years as a software engineer with Praxis Systems in Bath. Since the late 1980s, as a lecturer and now research fellow in the School of Informatics, University of Wales at Bangor, he has been developing the 'ICMAUS' framework of ideas which is the subject of this talk.
The Evening Lectures are free to both members and non-members of SGES.
For further information contact: Dr. Chris Christodoulou, Department of Computer Science, Birkbeck College chris@dcs.bbk.ac.uk.