Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization

Damiano Azzolini, Fabrizio Riguzzi
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:225-234, 2024.

Abstract

Probabilistic answer set programming has recently been extended to manage imprecise probabilities by means of credal probabilistic facts and credal annotated disjunctions. This increases the expressivity of the language but, at the same time, the cost of inference. In this paper, we cast inference in probabilistic answer set programs with credal probabilistic facts and credal annotated disjunctions as a constrained nonlinear optimization problem where the function to optimize is obtained via knowledge compilation. Empirical results on different datasets with multiple configurations shows the effectiveness of our approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v244-azzolini24a, title = {Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization}, author = {Azzolini, Damiano and Riguzzi, Fabrizio}, booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence}, pages = {225--234}, year = {2024}, editor = {Kiyavash, Negar and Mooij, Joris M.}, volume = {244}, series = {Proceedings of Machine Learning Research}, month = {15--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/azzolini24a/azzolini24a.pdf}, url = {https://proceedings.mlr.press/v244/azzolini24a.html}, abstract = {Probabilistic answer set programming has recently been extended to manage imprecise probabilities by means of credal probabilistic facts and credal annotated disjunctions. This increases the expressivity of the language but, at the same time, the cost of inference. In this paper, we cast inference in probabilistic answer set programs with credal probabilistic facts and credal annotated disjunctions as a constrained nonlinear optimization problem where the function to optimize is obtained via knowledge compilation. Empirical results on different datasets with multiple configurations shows the effectiveness of our approach.} }
Endnote
%0 Conference Paper %T Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization %A Damiano Azzolini %A Fabrizio Riguzzi %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-azzolini24a %I PMLR %P 225--234 %U https://proceedings.mlr.press/v244/azzolini24a.html %V 244 %X Probabilistic answer set programming has recently been extended to manage imprecise probabilities by means of credal probabilistic facts and credal annotated disjunctions. This increases the expressivity of the language but, at the same time, the cost of inference. In this paper, we cast inference in probabilistic answer set programs with credal probabilistic facts and credal annotated disjunctions as a constrained nonlinear optimization problem where the function to optimize is obtained via knowledge compilation. Empirical results on different datasets with multiple configurations shows the effectiveness of our approach.
APA
Azzolini, D. & Riguzzi, F.. (2024). Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:225-234 Available from https://proceedings.mlr.press/v244/azzolini24a.html.

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