logic programs with annotated disjunctions (LPADs) allow to express probabilistic information in logic programming. The semantics of an LPAD is given in terms of well founded models of the normal logicprograms obtain...
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(纸本)9783540899815
logic programs with annotated disjunctions (LPADs) allow to express probabilistic information in logic programming. The semantics of an LPAD is given in terms of well founded models of the normal logicprograms obtained by selecting one disjunct from each ground LPAD clause. The paper presents SLGAD resolution that computes the (conditional) probability of a ground query from an LPAD and is based on SLG resolution for normal logicprograms. SLGAD is evaluated on classical benchmarks for well founded semantics inference algorithms, namely the stalemate game and the ancestor relation. SLGAD is compared with Cilog2 and SLDNFAD, an algorithm based on SLDNF, on the programs that are modularly acyclic. The results show that SLGAD deals correctly with cyclic programs and, even if it is more expensive than SLDNFAD on problems where SLDNFAD succeeds, is faster than Cilog2 when the query is true in an exponential number of instances.
Probabilistic logic Programming is receiving an increasing attention for its ability to model domains with complex and uncertain relations among entities. In this paper we concentrate on the problem of approximate inf...
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Probabilistic logic Programming is receiving an increasing attention for its ability to model domains with complex and uncertain relations among entities. In this paper we concentrate on the problem of approximate inference in probabilistic logic programming languages based on the distribution semantics. A successful approximate approach is based on Monte Carlo sampling, that consists in verifying the truth of the query in a normal program sampled from the probabilistic program. The ProbLog system includes such an algorithm and so does the cplint suite. In this paper we propose an approach for Monte Carlo inference that is based on a program transformation that translates a probabilistic program into a normal program to which the query can be posed. The current sample is stored in the internal database of the Yap Prolog engine. The resulting system, called MCINTYRE for Monte Carlo INference wiTh Yap REcord, is evaluated on various problems: biological networks, artificial datasets and a hidden Markov model. MCINTYRE is compared with the Monte Carlo algorithms of ProbLog and cplint and with the exact inference of the PITA system. The results show that MCINTYRE is faster than the other Monte Carlo systems.
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, ri...
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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 logicprograms, 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 logicprograms 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.
Statistical Relational Learning and Probabilistic Inductive logic Programming are two emerging fields that use representation languages able to combine logic and probability. In the field of logic Programming, the dis...
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Statistical Relational Learning and Probabilistic Inductive logic Programming are two emerging fields that use representation languages able to combine logic and probability. In the field of logic Programming, the distribution semantics is one of the prominent approaches for representing uncertainty and underlies many languages such as ICL, PRISM, ProbLog and LPADs. Learning the parameters for such languages requires an Expectation Maximization algorithm since their equivalent Bayesian networks contain hidden variables. EMBLEM (EM over BDDs for probabilistic logicprograms Efficient Mining) is an EM algorithm for languages following the distribution semantics that computes expectations directly on the Binary Decision Diagrams that are built for inference. In this paper we present experiments comparing EMBLEM with LeProbLog, Alchemy, CEM, RIB and LFI-ProbLog on six real world datasets. The results show that EMBLEM is able to solve problems on which the other systems fail and it often achieves significantly higher areas under the Precision Recall and the ROC curves in a similar time.
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