Computer Science – Logic in Computer Science
Scientific paper
2011-07-26
Theory and Practice of Logic Programming, Volume 11, Special Issue 4-5, July 2011, pp 663-680. Cambridge University Press 2011
Computer Science
Logic in Computer Science
17 pages, 2 figures, International Conference on Logic Programming (ICLP 2011)
Scientific paper
10.1017/S1471068411000238
Today, many different probabilistic programming languages exist and even more inference mechanisms for these languages. Still, most logic programming based languages use backward reasoning based on SLD resolution for inference. While these methods are typically computationally efficient, they often can neither handle infinite and/or continuous distributions, nor evidence. To overcome these limitations, we introduce distributional clauses, a variation and extension of Sato's distribution semantics. We also contribute a novel approximate inference method that integrates forward reasoning with importance sampling, a well-known technique for probabilistic inference. To achieve efficiency, we integrate two logic programming techniques to direct forward sampling. Magic sets are used to focus on relevant parts of the program, while the integration of backward reasoning allows one to identify and avoid regions of the sample space that are inconsistent with the evidence.
Bruynooghe Maurice
Gutmann Bernd
Kimmig Angelika
Raedt Luc de
Thon Ingo
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