Bayesian Logic Programs

Abstract

Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations. We introduce a generalization of Bayesian networks, called Bayesian logic programs, to overcome these limitations. In order to represent objects and relations it combines Bayesian networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables. We show that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks. This includes the separation of quantitative and qualitative aspects of the model. Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs. So, many ideas developed

52 pages

References in corpus (4)

• Context-Specific Independence in Bayesian Networks
• Stochastic Simulation Algorithms for Dynamic Probabilistic Networks
• Bayes-Ball: The Rational Pastime (for Determining Irrelevance and Requisite Information in Belief Networks and Influence Diagrams)
• Loglinear models for first-order probabilistic reasoning

Cited by in corpus (8)

• Probabilistic Similarity Logic
• CLP(BN): Constraint Logic Programming for Probabilistic Knowledge
• The PITA System: Tabling and Answer Subsumption for Reasoning under Uncertainty
• Relational Dynamic Bayesian Networks
• Location-Based Reasoning about Complex Multi-Agent Behavior
• Inference in Probabilistic Logic Programs with Continuous Random Variables
• Probabilistic Inductive Logic Programming Based on Answer Set Programming
• Anytime Exact Belief Propagation