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 (Ginberg, Weiss, and Politakis 88) significantly expands the scope of the original system in terms of generality and automated capabilities. The investigators made significant progress in automating empirical expert system techniques for knowledge acquisition, knowledge base refinement, maintenance, and verification. The investigators demonstrated a rule refinement system in an application of the diagnosis of complex equipment failure: computer network troubleshooting. The expert system demonstrates the following advanced capabilities: 1) automatic localization of knowledge base weaknesses; 2) automatic repair (refinement) of poorly performing rules; 3) automatic verification of new knowledge base rules; and 4) automatic learning capabilities. Keywords: Expert system measurement; Medical expert systems.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 31, 1990
- Accession Number
- ADA220945
Entities
People
- Casimir A. Kulikowski
- Sholom M. Weiss
Organizations
- Rutgers University–New Brunswick