Inference in Probabilistic Logic Programs using Weighted CNF’s

Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:249-258, 2011.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-fierens11a, title = {Inference in Probabilistic Logic Programs using Weighted {CNF}’s}, author = {Fierens, Daan and Broeck, Guy Van den and Thon, Ingo and Gutmann, Bernd and De Raedt, Luc}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {249--258}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/fierens11a/fierens11a.pdf}, url = {https://proceedings.mlr.press/r9/fierens11a.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Inference in Probabilistic Logic Programs using Weighted CNF’s %A Daan Fierens %A Guy Van den Broeck %A Ingo Thon %A Bernd Gutmann %A Luc De Raedt %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-fierens11a %I PMLR %P 249--258 %U https://proceedings.mlr.press/r9/fierens11a.html %V R9 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Fierens, D., Broeck, G.V.d., Thon, I., Gutmann, B. & De Raedt, L.. (2011). Inference in Probabilistic Logic Programs using Weighted CNF’s. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:249-258 Available from https://proceedings.mlr.press/r9/fierens11a.html. Reissued by PMLR on 04 October 2026.

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