Lifted Variable Elimination for Probabilistic Logic Programming

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

Elena Bellodi (Autor) Outra; Evelina Lamma (Autor) Outra; Fabrizio Riguzzi (Autor) Outra; Vitor Santos Costa (Autor) FCUP; Riccardo Zese (Autor) Outra

Revista

Título: Theory and Practice of Logic Programming

Vol. 14

Páginas: 681-695

ISSN: 1471-0684

Editora: Cambridge University Press

Indexação

ISI Web of Knowledge - 12 Citações

Scopus - 17 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-009-PQZ

DOI: 10.1017/s1471068414000283

Abstract (EN): 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 LP2 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15