2013/04/25 by Daan Fierens, DAAN FIERENS, Guy Van den Broeck +13 · 1 citation
Computer Science · #Approximate inference #Bayesian Modeling and Causal Inference #Constraint Satisfaction and Optimization #Graphical model #Inference #Logic, Reasoning, and Knowledge #Probabilistic CTL #Probabilistic argumentation #Probabilistic logic #Probabilistic logic network #Rule of inference #cs.AI #cs.LG #cs.LO
paper · pdf · doi:10.1017/s1471068414000076
published as Theory and Practice of Logic Programming 15 (2015) 358-401 · To appear in Theory and Practice of Logic Programming (TPLP)
arxiv created 2013/04/25 · openalex publication_date 2014/04/15 · openalex created_date 2016/06/24 · arxiv updated 2020/02/19 · openalex updated_date 2026/08/06
Abstract Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks, such as computing the marginals, given evidence and learning from (partial) interpretations, have not really been addressed for probabilistic logic programs before. The first contribution of this paper is a suite of efficient algorithms for various inference tasks. It is based on the conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce inference tasks to well-studied tasks, such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs expectation-maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state of the art in probabilistic logic programming, and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.