Using an artificial neural system to determine the knowledge based of an expert system

  • Authors:

  • G. M. Whitson;Cathy Wu;Pam Taylor

  • Affiliations:

  • Computer Science Department, The University of Texas at Tyler, Tyler, Texas;Computer Science Department, The University of Texas at Tyler, Tyler, Texas;Computer Science Department, The University of Texas at Tyler, Tyler, Texas

  • Venue:
  • SIGSMALL '90 Proceedings of the 1990 ACM SIGSMALL/PC symposium on Small systems
  • Year:
  • 1990

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Abstract

This paper gives a mapping of rule based expert systems into artificial neural expert systems. While the mapping is not one-to-one, it does show that the two systems are essentially equivalent. There are, of course, many examples of artificial neural systems that are not expert systems. We can use the reverse mapping of an artificial neural expert system to a rule based expert system to determine the knowledge base of the rule based expert system, i.e., to determine the exact nature of the rules. This yields an automated procedure for determining the knowledge base of an expert system that shows much promise. We have implemented this expert system tool on several larger microcomputers, including an Intel Sugarcube. The Sugarcube implementation is a very natural one.