Inference and learning in probabilistic logic programs using weighted Boolean formulas

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 comput...

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Vydáno v:Theory and practice of logic programming Ročník 15; číslo 3; s. 358 - 401
Hlavní autoři: FIERENS, DAAN, VAN DEN BROECK, GUY, RENKENS, JORIS, SHTERIONOV, DIMITAR, GUTMANN, BERND, THON, INGO, JANSSENS, GERDA, DE RAEDT, LUC
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cambridge, UK Cambridge University Press 01.05.2015
Témata:
Algorithms
Inference
Learning
Logic programming
Mathematical models
Parameter learn-ing
Probabilistic inference
Probabilistic logic programming
Probabilistic methods
Probability theory
Regular Papers
Tasks
probabilistic inference
parameter learning
probabilistic logic programming
ISSN:1471-0684, 1475-3081, 1475-3081
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Shrnutí: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.
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ISSN:1471-0684
1475-3081
1475-3081
DOI:10.1017/S1471068414000076