Date of Award

3-1995

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Eugene Santos, Jr., PhD

Abstract

This thesis develops a methodology and a tool for knowledge acquisition with the new probabilistic knowledge representation-the Bayesian Forest. It establishes the structure of the Knowledge Acquisition and Maintenance module of the Probabilities. Expert Systems, Knowledge and Inference (PESKI) architecture. The tool, MACK, is designed to be used directly by the domain expert(s) rather than by knowledge engineer(s), and thus supports automated knowledge acquisition. This research determines and implements the constraints necessary to ensure the consistency of Bayesian Forest knowledge bases as data is both acquired and subsequently maintained. The impact to the PESKI architecture of time-dependent information and default assumptions during reasoning is also explored. The tool has been applied to NASA's Post-Test Diagnostics System which locates anomalies aboard the Space Shuttles' Main Engines.

AFIT Designator

AFIT-GSO-ENG-95M-01

DTIC Accession Number

ADA293858

Comments

The author's Vita page is omitted.

Dual-degree thesis.

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