The well-founded semantics for general logic programs
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Machine Learning
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Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
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PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
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AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
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ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
A Simple Model for Sequences of Relational State Descriptions
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ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Inference with Logic Programs with Annotated Disjunctions under the Well Founded Semantics
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
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AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Cp-logic: A language of causal probabilistic events and its relation to logic programming
Theory and Practice of Logic Programming
Evaluating abductive hypotheses using an EM algorithm on BDDs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
MCINTYRE: A Monte Carlo System for Probabilistic Logic Programming
Fundamenta Informaticae - Special Issue on the Italian Conference on Computational Logic: CILC 2011
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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 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 Expectation Maximization EM algorithm is adopted. In order to speed the computation up, expectations are computed directly on the Binary Decision Diagrams 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 good quality of the solutions.