Recently much work in Machine Learning has concentrated on using expressive representation languages that combine aspects of logic and probability. A whole field has emerged, called Statistical Relational Learning, rich of successful applications in a variety of domains. In this paper we present a Machine Learning technique targeted to Probabilistic Logic Programs, a family of formalisms where uncertainty is represented using Logic Programming tools. Among various proposals for Probabilistic Logic Programming, the one based on the distribution semantics is gaining popularity and is the basis for a number of languages, such as ICL, PRISM, ProbLog and Logic Programs with Annotated Disjunctions. This paper proposes a technique for learning parameters of these languages. Since their equivalent Bayesian networks contain hidden variables, an EM algorithm is adopted. In order to speed the computation up, expectations are computed directly on the Binary Decision Diagrams structures that are built for inference. The resulting system, called Emblem for "EM over BDDs for probabilistic Logic programs Efficient Mining", has been applied to a number of datasets and showed good performances both in terms of speed and memory usage. In particular its speed allows the execution of a high number of restarts, resulting in better solutions quality.

EM over Binary Decision Diagrams for Probabilistic Logic Programs

BELLODI, Elena;RIGUZZI, Fabrizio
2011

Abstract

Recently much work in Machine Learning has concentrated on using expressive representation languages that combine aspects of logic and probability. A whole field has emerged, called Statistical Relational Learning, rich of successful applications in a variety of domains. In this paper we present a Machine Learning technique targeted to Probabilistic Logic Programs, a family of formalisms where uncertainty is represented using Logic Programming tools. Among various proposals for Probabilistic Logic Programming, the one based on the distribution semantics is gaining popularity and is the basis for a number of languages, such as ICL, PRISM, ProbLog and Logic Programs with Annotated Disjunctions. This paper proposes a technique for learning parameters of these languages. Since their equivalent Bayesian networks contain hidden variables, an EM algorithm is adopted. In order to speed the computation up, expectations are computed directly on the Binary Decision Diagrams structures that are built for inference. The resulting system, called Emblem for "EM over BDDs for probabilistic Logic programs Efficient Mining", has been applied to a number of datasets and showed good performances both in terms of speed and memory usage. In particular its speed allows the execution of a high number of restarts, resulting in better solutions quality.
2011
Statistical Relational Learning; Probabilistic Inductive Logic Programming; Probabilistic Logic Programs; Logic Programs with Annotated Disjunctions; Expectation Maximization; Binary Decision Diagrams
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1506919
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