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Knowledge acquisition tools based on personal construct psychology

Published online by Cambridge University Press:  07 July 2009

Brian R. Gaines  and
Mildred L. G. Shaw
Brian R. Gaines
Affiliation:
Knowledge Science Institute, University of Calgary, Calgary, Alberta, Canada T2N 1N4gaines@cpsc.ucalgary.ca & mildred@cpsc.ucalgary.ca
Mildred L. G. Shaw
Affiliation:
Knowledge Science Institute, University of Calgary, Calgary, Alberta, Canada T2N 1N4gaines@cpsc.ucalgary.ca & mildred@cpsc.ucalgary.ca
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Abstract

Knowledge acquisition research supports the generation of knowledge-based systems through the development of principles, techniques, methodologies and tools. What differentiates knowledge-based system development from conventional system development is the emphasis on in-depth understanding and formalization of the relations between the conceptual structures underlying expert performance and the computational structures capable of emulating that performance.

Personal construct psychology is a theory of individual and group psychological and social processes that has been used extensively in knowledge acquisition research to model the cognitive processes of human experts. The psychology takes a constructivist position appropriate to the modelling of human knowledge processes, but develops this through the characterization of human conceptual structures in axiomatic terms that translate directly to computational form. In particular, there is a close correspondence between the intensional logics of knowledge, belief and action developed in personal construct psychology, and the intensional logics for formal knowledge representation developed in artificial intelligence research as term subsumption, or KL-ONE-like, systems.

This paper gives an overview of personal construct psychology and its expression as an intensional logic describing the cognitive processes of anticipatory agents, and uses this to survey knowledge acquisition tools deriving from personal construct psychology.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 8 , Issue 1 , March 1993 , pp. 49 - 85
Copyright
Copyright © Cambridge University Press 1993

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