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What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems

Published online by Cambridge University Press:  07 July 2009

E. T. Keravnou  and
J. Washbrook
E. T. Keravnou
Affiliation:
Department of Computer Science, University College, London, Gower Street, London WC1E 6BT, UK
J. Washbrook
Affiliation:
Department of Computer Science, University College, London, Gower Street, London WC1E 6BT, UK
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Abstract

First-generation expert systems have significant limitations, often attributed to their not being sufficiently deep. However, a generally accepted answer to “What is a deep expert system?” is still to be given. To answer this question one needs to answer “Why do first-generation systems exhibit the limitations they do?” thus identifying what is missing from first-generation systems and therefore setting the design objectives for second-generation (i.e. deep) systems. Several second-generation architectures have been proposed; inherent in each of these architectures is a definition of deepness. Some of the proposed architectures have been designed with the objective of alleviating a subset, rather than the whole set, of the first-generation limitations. Such approaches are prone to local, non-robust solutions. In this paper we analyze the limitations (under the categories: human-computer interaction, problem-solving flexibility, and extensibility) of the first-generation expert systems thus setting design goals for second-generation systems. On the basis of this analysis proposed second-generation architectures are reviewed and compared. The paper concludes by presenting requirements for a generic second-generation architecture.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 4 , Issue 3 , September 1989 , pp. 205 - 233
Copyright
Copyright © Cambridge University Press 1989

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