k-Optimal: a novel approximate inference algorithm for ProbLog

Renkens, Joris; Broeck, Guy Van den; Nijssen, Siegfried

doi:10.1007/s10994-012-5304-9

Cited by 4 publications

References 6 publications

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10 Years of Probabilistic Querying – What Next?

Theobald

Raedt

Dylla

et al. 2013

Advances in Databases and Information Systems

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Abstract. Over the past decade, the two research areas of probabilistic databases and probabilistic programming have intensively studied the problem of making structured probabilistic inference scalable, but-so far-both areas developed almost independently of one another. While probabilistic databases have focused on describing tractable query classes based on the structure of query plans and data lineage, probabilistic programming has contributed sophisticated inference techniques based on knowledge compilation and lifted (first-order) inference. Both fields have developed their own variants of-both exact and approximate-top-k algorithms for query evaluation, and both investigate query optimization techniques known from SQL, Datalog, and Prolog, which all calls for a more intensive study of the commonalities and integration of the two fields. Moreover, we believe that natural-language processing and information extraction will remain a driving factor and in fact a longstanding challenge for developing expressive representation models which can be combined with structured probabilistic inference-also for the next decades to come.

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10 Years of Probabilistic Querying – What Next?

Theobald

Raedt

Dylla

et al. 2013

Advances in Databases and Information Systems

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Probabilistic (logic) programming concepts

2015

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A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages.To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position state-of-the-art probabilistic languages and their implementation.While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been developed since more than 20 years.

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TP-Compilation for inference in probabilistic logic programs

Vlasselaer

Broeck

Kimmig

et al. 2016

International Journal of Approximate Reasoning

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We propose T P -compilation, a new inference technique for probabilistic logic programs that is based on forward reasoning. T P -compilation proceeds incrementally in that it interleaves the knowledge compilation step for weighted model counting with forward reasoning on the logic program. This leads to a novel anytime algorithm that provides hard bounds on the inferred probabilities. The main difference with existing inference techniques for probabilistic logic programs is that these are a sequence of isolated transformations. Typically, these transformations include conversion of the ground program into an equivalent propositional formula and compilation of this formula into a more tractable target representation for weighted model counting. An empirical evaluation shows that T P -compilation effectively handles larger instances of complex or cyclic real-world problems than current sequential approaches, both for exact and anytime approximate inference. Furthermore, we show that T P -compilation is conducive to inference in dynamic domains as it supports efficient updates to the compiled model.

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k-Optimal: a novel approximate inference algorithm for ProbLog

Cited by 4 publications

References 6 publications

10 Years of Probabilistic Querying – What Next?

10 Years of Probabilistic Querying – What Next?

Probabilistic (logic) programming concepts

TP-Compilation for inference in probabilistic logic programs

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