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, significantly expands the scope of the original system in terms of generality and automated capabilities. Based on promising results using the SEEK approach, significant progress can be made in expert system techniques for knowledge acquisition, knowledge base refinement, maintenance, and verification. Artificial intelligence.

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Document Details

Document Type
Technical Report
Publication Date
Aug 31, 1988
Accession Number
ADA200146

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
  • Computer Science
  • Computers
  • Data Analysis
  • Data Sets
  • Databases
  • Demonstrations
  • Expert Systems
  • Knowledge Based Systems
  • Learning
  • Machine Learning
  • Maintenance
  • Observation
  • Pattern Recognition

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Organizational Process Management (OPM).
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy