Date of Award

12-1995

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Eugene Santos, PhD

Abstract

Many real world domains cannot be represented using Bayesian Networks due to the need for complete probability tables and acyclic knowledge. However, Bayesian Knowledge Bases (BKBs) are a viable method for representing these incomplete domains, but very little research has been performed on inferencing with them. This paper presents three inference engines for extracting optimal solutions from three distinct BKB subclasses: singly- connected, multiply-connected with mutually exclusive cycles, and cyclic. The singly-connected inference engine has a worst case polynomial run time. Performance improvement techniques for increasing inference engine speed are discussed, in addition to a new tool for measuring incompleteness and aiding in BKB Validation & Verification.

AFIT Designator

AFIT-GCS-ENG-95D-08

DTIC Accession Number

ADA303826

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