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