Probabilistic Logic Programming and Bayesian Networks
Liem Ngo and Peter Haddawy Department of Electrical Engineering and Computer Science University of WisconsinMilwaukee Milwaukee, WI 53201
We present a probabilistic logic programming framework that allows the repre sentation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming. We de fine a fixpoint theory, declarative semantics, and proof procedure for the new class of probabilistic logic programs. Compared to other approaches to quantitative logic programming, we provide a true probabilistic framework with potential applications in probabilistic expert systems and decision support systems. We also discuss the relation ship between such programs and Bayesian networks, thus moving toward a unification of two major approaches to automated reasoning.