2024-09-12-azzolini24a.md

title	abstract	openreview	software	section	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization	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.	h5VFqO681Y	https://github.com/damianoazzolini/aspmc	Papers	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	azzolini24a	0	Inference in Probabilistic Answer Set Programs with Imprecise Probabilities via Optimization	225	234	225-234	225	false	Azzolini, Damiano and Riguzzi, Fabrizio	given family Damiano Azzolini given family Fabrizio Riguzzi	2024-09-12		Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence	244	inproceedings	date-parts 2024 9 12	https://raw.githubusercontent.com/mlresearch/v244/main/assets/azzolini24a/azzolini24a.pdf