Probabilistic Logic for Belief Nets
Date of Original Version
Abstract or Description
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Bayesian logic") for dealing with uncertainty and causal relationships in an expert system. Probabilistic logic, invented by Boole, is a technique for drawing inferences from uncertain propositions for which there are no independence assumptions. A Bayesian network is a "belief net" that can represent complex conditional independence assumptions. We show how to solve inference problems in Bayesian logic by applying Benders decomposition to a nonlinear programming formulation. We also show that the number of constraints grows only linearly with the problem size for a large class of networks.
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Proceeedings, 8th International Congress of Cybernetics and Systems, New York City, 245-251.