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Inference and learning in probabilistic logic programs using weighted Boolean formulas

Published online by Cambridge University Press:  15 April 2014

DAAN FIERENS
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001 Heverlee, BelgiumFirstName.LastName@cs.kuleuven.be
GUY VAN DEN BROECK
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001 Heverlee, BelgiumFirstName.LastName@cs.kuleuven.be
JORIS RENKENS
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001 Heverlee, BelgiumFirstName.LastName@cs.kuleuven.be
DIMITAR SHTERIONOV
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001 Heverlee, BelgiumFirstName.LastName@cs.kuleuven.be
BERND GUTMANN
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001 Heverlee, BelgiumFirstName.LastName@cs.kuleuven.be
INGO THON
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001 Heverlee, BelgiumFirstName.LastName@cs.kuleuven.be
GERDA JANSSENS
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001 Heverlee, BelgiumFirstName.LastName@cs.kuleuven.be
LUC DE RAEDT
Affiliation:
Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001 Heverlee, BelgiumFirstName.LastName@cs.kuleuven.be
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Abstract

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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.

Type
Regular Papers
Copyright
Copyright © Cambridge University Press 2014 

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