Inferencing Over Incomplete Solution Spaces with Genetic Algorithms for Probabilistic Reasoning

Brett J. Borghetti - Edward M. Williams - Eugene Santos, Jr.
Department of Electrical and Computer Engineering
Air Force Institute of Technology
Wright-Patterson AFB, OH 45433-7765
{bborghet,ewilliam,esantos}@afit.af.mil

February 15, 1996

Abstract:

We develop a new method for handling incomplete knowledge when inferencing over Bayesian Knowledge Bases(BKB) using genetic algorithms(GA) . The fitness function for a genetic algorithm requires that we give a score to each solution it generates. When a solution is complete, we can use the joint probability of our generated solution, but when incompleteness occurs, the joint probability is undefined. In this paper, we characterize incompleteness in BKBs and present an easily computable method for scoring a solution which contains incompleteness.

This research was supported in part by AFOSR Project #940006.

• Introduction
• Bayesian Knowledge Bases
• Belief Revision and Genetic Algorithms
• Accounting for Incompleteness
• Test Methodology
• Conclusion
• References
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