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The potential of machine learning techniques for expert systems

Published online by Cambridge University Press:  27 February 2009

Yoram Reich  and
Steven J. Fenves
Yoram Reich
Affiliation:
Department of Civil Engineering, Carnegie-Mellon University, Pittsburgh, PA 15213, U.S.A.
Steven J. Fenves
Affiliation:
Department of Civil Engineering, Carnegie-Mellon University, Pittsburgh, PA 15213, U.S.A.
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Abstract

Expert systems employing current methodologies suffer from two major problems: they are brittle and their development is time-consuming and tedious. Learning, the key to intelligent human behavior and expertise, has the potential of alleviating these difficulties. The paper reviews a number of machine learning techniques and provides a framework for their classification. The description of each technique is followed by an example taken from the domain of structural design. The applicability of machine learning techniques to expert systems is discussed, including some prototype applications and their shortcomings. Three promising research directions are outlined as a partial solution for the shortcomings.

Type
Research Article
Information
AI EDAM , Volume 3 , Issue 3 , August 1989 , pp. 175 - 193
Copyright
Copyright © Cambridge University Press 1989

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