Uncertainty in Artificial Intelligence
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Inference in Probabilistic Logic Programs using Weighted CNF's
Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt
Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. Several classical probabilistic inference tasks (such as MAP and computing marginals) have not yet received a lot of attention for this formalism. The contribution of this paper is that we develop efficient inference algorithms for these tasks. This is based on a conversion of the probabilistic logic program and the query and evidence to a weighted CNF formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting. To solve such tasks, we employ state-of-the-art methods. We consider multiple methods for the conversion of the programs as well as for inference on the weighted CNF. The resulting approach is evaluated experimentally and shown to improve upon the state-of-the-art in probabilistic logic programming.
Pages: 211-220
PS Link:
PDF Link: /papers/11/p211-fierens.pdf
AUTHOR = "Daan Fierens and Guy Van den Broeck and Ingo Thon and Bernd Gutmann and Luc De Raedt",
TITLE = "Inference in Probabilistic Logic Programs using Weighted CNF's",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "211--220"

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