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Proceedings Paper

Hybrid system architecture for reasoning in noisy domains
Author(s): David G. Melvin; C. Tim Spracklen
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Paper Abstract

Neural network techniques and those used in conventional artificial intelligence systems show promise for solving complex real world problems. The strengths and weaknesses of the separate techniques are well known, for example the former is capable of reasoning on noisy or incomplete data but has poor facilities for eliciting input/output correlations. On the other hand, the latter has poor domain knowledge elicitation but is much better at explaining the correlation between its input data and its output response. In certain valuable aspects the two techniques are complementary and this paper reports on the characteristics exhibited by a hybrid system formed from a set of neural networks and a classical expert system. The paper describes how a hybrid system based upon a number of artificial neural network subsystems (ANNS), each implementing a knowledge source, is attached to a rule based system. By fusing these neural networks into the knowledge based system in a transparent way a new architecture is formed which performs markedly better than a conventional rule based system while retaining the explanation facilities of the rule based approach. The paper discusses in detail a prototype system which operates in the domain of optical character recognition. The paper highlights the advantages and disadvantages of such a technique in terms of concepts that are applicable to many other real world problem domains.

Paper Details

Date Published: 16 September 1992
PDF: 7 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140074
Show Author Affiliations
David G. Melvin, Univ. of Aberdeen (United Kingdom)
C. Tim Spracklen, Univ. of Aberdeen (United Kingdom)

Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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