2012/02/14 by Daan Fierens, Fierens, Daan, Guy Van den Broeck +7 · 72 citations
Computer Science · #Algorithm #Artificial intelligence #Autoepistemic logic #Bayesian Modeling and Causal Inference #Computer science #Description logic #Formalism (music) #Inference #Logic programming #Logic, Reasoning, and Knowledge #Machine learning #Multimodal logic #Probabilistic CTL #Probabilistic analysis of algorithms #Probabilistic argumentation #Probabilistic logic #Probabilistic logic network #Rule of inference #Semantic Web and Ontologies #Theoretical computer science #Variable elimination #cs.AI
paper · pdf · doi:10.48550/arxiv.1202.3719
published in arXiv (Cornell University), 211-220 (Cornell University)
arxiv created 2012/02/14 · openalex publication_date 2012/02/14 · arxiv updated 2012/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is that we develop efficient inference algorithms for these tasks. This is based on a conversion of the probabilistic logic program and the query and evidence to a weighted CNF formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting. To solve such tasks, we employ state-of-the-art methods. We consider multiple methods for the conversion of the programs as well as for inference on the weighted CNF. The resulting approach is evaluated experimentally and shown to improve upon the state-of-the-art in probabilistic logic programming.