{"type":"ScholarlyArticle","title":"Efficient Weighted Model Integration via SMT-Based Predicate Abstraction","authors":[{"name":"Paolo Morettin","slug":"paolo-morettin","given":"Paolo","family":"Morettin","affiliation":"DISI , University of Trento , Italy."},{"name":"Andrea Passerini","slug":"andrea-passerini","given":"Andrea","family":"Passerini","affiliation":"DISI , University of Trento , Italy."},{"name":"Roberto Sebastiani","slug":"roberto-sebastiani","given":"Roberto","family":"Sebastiani","affiliation":"DISI , University of Trento , Italy."}],"abstract":"Weighted model integration (WMI) is a recent formalism generalizing weighted model counting (WMC) to run probabilistic inference over hybrid domains, characterized by both discrete and continuous variables and relationships between them. Albeit powerful, the original formulation of WMI suffers from some theoretical limitations, and it is computationally very demanding as it requires to explicitly enumerate all possible models to be integrated over. In this paper we present a novel general notion of WMI, which fixes the theoretical limitations and allows for exploiting the power of SMT-based predicate abstraction techniques. A novel algorithm combines a strong reduction in the number of models to be integrated over with their efficient enumeration. Experimental results on synthetic and real-world data show drastic computational improvements over the original WMI formulation as well as existing alternatives for hybrid inference.","doi":"10.24963/ijcai.2017/100","url":"https://ijcai.org/proceedings/2017/100","pdf":"https://ijcai.org/proceedings/2017/0100.pdf","markdown":"https://ijcai.org/proceedings/2017/100.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/100","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Constraints and Satisfiability","track":"Main track","number":100,"pages":{"first":720,"last":728},"keywords":["Uncertainty in AI: Relational Inference","Uncertainty in AI: Exact Probabilistic Inference","Constraints and Satisfiability: Constraints and Satisfiability"],"videos":[],"references":[{"position":1,"raw":"WMI-BC and WMI-ALLSMT are not considered here as their execution times are prohibitive for all but the smallest path lengths. 7 Note that the complexity of the query is due to the combination of the path length and the number of time intervals in which the time horizon is divided (M=12 in these experiments). 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