{"type":"ScholarlyArticle","title":"Encoding Probabilistic Graphical Models into Stochastic Boolean Satisfiability","authors":[{"name":"Cheng-Han Hsieh","slug":"cheng-han-hsieh","given":"Cheng-Han","family":"Hsieh","affiliation":"Graduate Institute of Electronics Engineering , National Taiwan University , Taipei , Taiwan"},{"name":"Jie-Hong R. Jiang","slug":"jie-hong-r-jiang","given":"Jie-Hong R.","family":"Jiang","affiliation":"Graduate Institute of Electronics Engineering , National Taiwan University , Taipei , Taiwan; Department of Electrical Engineering , National Taiwan University , Taipei , Taiwan"}],"abstract":"Statistical inference is a powerful technique in various applications. Although many statistical inference tools are available, answering inference queries involving complex quantification structures remains challenging. Recently, solvers for Stochastic Boolean Satisfiability (SSAT), a powerful formalism allowing concise encodings of PSPACE decision problems under uncertainty, are under active development and applied in more and more applications. In this work, we exploit SSAT solvers for the inference of Probabilistic Graphical Models (PGMs), an essential representation for probabilistic reasoning. Specifically, we develop encoding methods to systematically convert PGM inference problems into SSAT formulas for effective solving. Experimental results demonstrate that, by using our encoding, SSAT-based solving can complement existing PGM tools, especially in answering complex queries.","doi":"10.24963/ijcai.2022/255","url":"https://ijcai.org/proceedings/2022/255","pdf":"https://ijcai.org/proceedings/2022/0255.pdf","markdown":"https://ijcai.org/proceedings/2022/255.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/255","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":255,"pages":{"first":1834,"last":1842},"keywords":["Constraint Satisfaction and Optimization: Modeling","Constraint Satisfaction and Optimization: Constraint Satisfaction","Constraint Satisfaction and Optimization: Solvers and Tools","Constraint Satisfaction and Optimization: Satisfiabilty","Constraint Satisfaction and Optimization: Applications"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986794","length_sec":75,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984937","length_sec":355,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bart et al., 2016] Anicet Bart, Frédéric Koriche, Jean-Marie Lagniez, and Pierre Marquis. 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