{"type":"ScholarlyArticle","title":"Most Probable Explanation in Probabilistic Answer Set Programming","authors":[{"name":"Damiano Azzolini","slug":"damiano-azzolini","given":"Damiano","family":"Azzolini","affiliation":""},{"name":"Giuseppe Mazzotta","slug":"giuseppe-mazzotta","given":"Giuseppe","family":"Mazzotta","affiliation":""},{"name":"Francesco Ricca","slug":"francesco-ricca","given":"Francesco","family":"Ricca","affiliation":""},{"name":"Fabrizio Riguzzi","slug":"fabrizio-riguzzi","given":"Fabrizio","family":"Riguzzi","affiliation":""}],"abstract":"Most Probable Explanation (MPE) is a fundamental problem in statistical relational artificial intelligence.\r\nIn the context of Probabilistic Answer Set Programming (PASP), solving MPE is still an open research problem.\r\nIn this paper, we present three novel approaches for solving the MPE task in PASP that are based on: i) Algebraic Model Counting, ii) Answer Set Programming (ASP), and iii) ASP with quantifiers (ASP(Q)).\r\nThese approaches are implemented and evaluated against existing solvers across different datasets and configurations.\r\nEmpirical results demonstrate that the novel solutions consistently outperform existing alternatives for non-stratified programs.","doi":"10.24963/ijcai.2025/1006","url":"https://ijcai.org/proceedings/2025/1006","pdf":"https://ijcai.org/proceedings/2025/1006.pdf","markdown":"https://ijcai.org/proceedings/2025/1006.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1006","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Uncertainty in AI","track":"Main Track","number":1006,"pages":{"first":9049,"last":9057},"keywords":["Uncertainty in AI: UAI: Uncertainty representations","Uncertainty in AI: UAI: Statistical relational AI","Knowledge Representation and Reasoning: KRR: Learning and reasoning","Knowledge Representation and Reasoning: KRR: Non-monotonic reasoning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}