2001/11/23 by Kristian Kersting, Luc De Raedt, Kersting, Kristian +1 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #F.3.2 #F.4.1 #FOS: Computer and information sciences #G.3 #I.2 #I.2.3 #I.2.4 #Logic in Computer Science (cs.LO) #Logic, Reasoning, and Knowledge #Semantic Web and Ontologies #cs.AI #cs.LO
paper · pdf · doi:10.48550/arxiv.cs/0111058
52 pages
arxiv created 2001/11/23 · openalex publication_date 2001/11/23 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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