Accession Number : ADA306138

Title :   Probabilistic Knowledge Base Validation.

Descriptive Note : Master's thesis,

Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING

Personal Author(s) : Gleason, Howard T.

PDF Url : ADA306138

Report Date : DEC 1995

Pagination or Media Count : 89

Abstract : Our work develops a new methodology and tool for the validation of probabilistic knowledge bases throughout their lifecycle. The methodology minimizes user interaction by automatically modifying incorrect knowledge; only the occurrence of incomplete knowledge involves interaction. These gains are realized by combining and modifying techniques borrowed from rule-based and artificial neural network validation strategies. The presented methodology is demonstrated through BVAL, which is designed for a new knowledge representation, the Bayesian Knowledge Base. This knowledge representation accommodates incomplete knowledge while remaining firmly grounded in probability theory.

Descriptors :   *VALIDATION, *KNOWLEDGE BASED SYSTEMS, METHODOLOGY, NEURAL NETS, INTERACTIONS, THEORY, PROBABILITY, USER NEEDS, BAYES THEOREM.

Subject Categories : Computer Systems

Distribution Statement : APPROVED FOR PUBLIC RELEASE