Empirical Analysis and Refinement of Expert System Knowledge Bases

Abstract

Knowledge base refinement is the modification of an existing expert system knowledge base with the goals of localizing specific weaknesses in a knowledge base and improving an expert system's performance. Systems that automate some aspects of knowledge base refinement can have a significant impact on the related problems of knowledge base acquisition, maintenance, verification, and learning from experience. The SEEK system was the first expert system framework to integrate large-scale performance information into all phases of knowledge base development and to provide automatic information about rule refinement. A recently developed successor system, SEEK2(Ginsberg, Weiss, and Politakis 88) significantly expands the scope of the original system in terms of generality and automated capabilities. The investigators expect to make significant progress in automating empirical expert system techniques for knowledge acquisition, knowledge base refinement, maintenance and verification. The investigators will demonstrate a rule refinement system in an application of the diagnosis of complex equipment failure: computer network troubleshooting. (kr)

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Document Details

Document Type
Technical Report
Publication Date
Aug 31, 1989
Accession Number
ADA213240

Entities

People

  • Casimir A. Kulikowski
  • Sholom M. Weiss

Organizations

  • Rutgers University–New Brunswick

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Acquisition
  • Artificial Intelligence
  • Automatic
  • Computer Networks
  • Contracts
  • Data Sets
  • Estimators
  • Expert Systems
  • Induction Systems
  • Language
  • Learning
  • Machine Learning
  • Military Research
  • Pattern Recognition
  • Simulations
  • Simulators
  • Troubleshooting

Readers

  • Artificial Intelligence