2014/05/31 by Elena Bellodi, Evelina Lamma, Fabrizio Riguzzi +3
Computer Science · Mathematics · #Artificial intelligence #Autoepistemic logic #Bayesian Modeling and Causal Inference #Computer science #Description logic #Independence (probability theory) #Inference #Logic programming #Logic, Reasoning, and Knowledge #Mathematics #Multimodal logic #Probabilistic argumentation #Probabilistic logic #Probabilistic logic network #Programming language #Prolog #Random variable #Semantic Web and Ontologies #Semantics (computer science) #Theoretical computer science #Variable (mathematics) #Variable elimination #cs.AI
paper · pdf · doi:10.1017/s1471068414000283
published as Theory and Practice of Logic Programming 14 (2014) 681-695 · To appear in Theory and Practice of Logic Programming (TPLP). arXiv admin note: text overlap with arXiv:1402.0565 by other authors
openalex publication_date 2014/07/01 · arxiv created 2014/10/10 · arxiv updated 2020/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relational languages outside of logic programming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. These factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. The resulting algorithm, called LP 2 for Lifted Probabilistic Logic Programming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.