CP-Logic Theory Inference with Contextual Variable Elimination and Comparison to BDD Based Inference Methods

Meert, Wannes; Struyf, Jan; Blockeel, Hendrik

doi:10.1007/978-3-642-13840-9_10

Cited by 19 publications

(29 citation statements)

References 13 publications

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“…It underlies for example Probabilistic Logic Programs [2], Probabilistic Horn Abduction (PHA) [22], PRISM [32], Independent Choice Logic (ICL) [23], pD [8], Logic Programs with Annotated Disjunctions (LPADs) [41], ProbLog [5] and CPlogic [39]. The approach is particularly appealing for its intuitiveness and because efficient inference algorithms have started to appear [5,15,20,27,29]. Most of these techniques use Binary Decision Diagrams (BDD) for inference: explanations for the query are found and the probability of the query is computed by building a BDD.…”

Section: Introductionmentioning

confidence: 99%

Expectation maximization over binary decision diagrams for probabilistic logic programs

Bellodi

Riguzzi

2013

IDA

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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.

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Section: Introductionmentioning

confidence: 99%

Expectation maximization over binary decision diagrams for probabilistic logic programs

Bellodi

Riguzzi

2013

IDA

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“…Each core has an additional 8 KiB of private memory available. Our experiments use the Bloodtype, GrowingHead and GrowingBody benchmarks from [10]. The latter two benchmarks consist of ground programs.…”

Section: Resultsmentioning

confidence: 99%

“…• The approach of [9], which transforms CP-logic into a Bayesian network and then applies the Variable Elimination (VE) algorithm or Contextual Variable Elimination (CVE) [10] • The default and Monte Carlo inference algorithms of the ProbLog system [7] • The PITA algorithm [18] that is built into XSB Prolog • The MCINTYRE backwards sampling algorithms [15].…”

Section: Resultsmentioning

confidence: 99%

An OpenCL implementation of a forward sampling algorithm for CP-logic

Ranst

Vennekens

2015

International Journal of Approximate Reasoning

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We present an approximate query answering algorithm for the Probabilistic Logic Programming language CP-logic. It complements existing sampling algorithms by using the rules from body to head instead of in the other direction. We present an implementation in OpenCL, which is able to exploit the multicore architecture of modern GPUs to compute a large number of samples in parallel, and demonstrate that this is competitive with existing inference algorithms.

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“…To compute the probability of a query given a set of atoms, one needs to use an inference algorithm such as De Raedt et al (2007), Riguzzi (2007bRiguzzi ( , 2008bRiguzzi ( , 2010, Meert et al (2010), Riguzzi and Swift (2010).…”

Section: Distribution Semanticsmentioning

confidence: 99%

Applying the information bottleneck to statistical relational learning

Riguzzi

Mauro

2011

Mach Learn

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In this paper we propose to apply the Information Bottleneck (IB) approach to the sub-class of Statistical Relational Learning (SRL) languages that are reducible to Bayesian networks. When the resulting networks involve hidden variables, learning these languages requires the use of techniques for learning from incomplete data such as the Expectation Maximization (EM) algorithm. Recently, the IB approach was shown to be able to avoid some of the local maxima in which EM can get trapped when learning with hidden variables. Here we present the algorithm Relational Information Bottleneck (RIB) that learns the parameters of SRL languages reducible to Bayesian Networks. In particular, we present the specialization of RIB to a language belonging to the family of languages based on the distribution semantics, Logic Programs with Annotated Disjunction (LPADs). This language is prototypical for such a family and its equivalent Bayesian networks contain hidden variables. RIB is evaluated on the IMDB, Cora and artificial datasets and compared with LeProbLog, EM, Alchemy and PRISM. The experimental results show that RIB has good performances especially when some logical atoms are unobserved. Moreover, it is particularly suitable when learning from interpretations that share the same Herbrand base.

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CP-Logic Theory Inference with Contextual Variable Elimination and Comparison to BDD Based Inference Methods

Cited by 19 publications

References 13 publications

Expectation maximization over binary decision diagrams for probabilistic logic programs

Expectation maximization over binary decision diagrams for probabilistic logic programs

An OpenCL implementation of a forward sampling algorithm for CP-logic

Applying the information bottleneck to statistical relational learning

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