{"type":"ScholarlyArticle","title":"Solving Exist-Random Quantified Stochastic Boolean Satisfiability via Clause Selection","authors":[{"name":"Nian-Ze Lee","slug":"nian-ze-lee","given":"Nian-Ze","family":"Lee","affiliation":"Graduate Institute of Electronics Engineering , National Taiwan University , Taipei , Taiwan"},{"name":"Yen-Shi Wang","slug":"yen-shi-wang","given":"Yen-Shi","family":"Wang","affiliation":"Department of Electrical 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":"Stochastic Boolean satisfiability (SSAT) is an expressive language to formulate decision problems with randomness. Solving SSAT formulas has the same PSPACE-complete computational complexity as solving quantified Boolean formulas (QBFs). Despite its broad applications and profound theoretical values, SSAT has received relatively little attention compared to QBF. In this paper, we focus on exist-random quantified SSAT formulas, also known as E-MAJSAT, which is a special fragment of SSAT commonly applied in probabilistic conformant planning, posteriori hypothesis, and maximum expected utility. Based on clause selection, a recently proposed QBF technique, we propose an algorithm to solve E-MAJSAT. Moreover, our method can provide an approximate solution to E-MAJSAT with a lower bound when an exact answer is too expensive to compute. Experiments show that the proposed algorithm achieves significant performance gains and memory savings over the state-of-the-art SSAT solvers on a number of benchmark formulas, and provides useful lower bounds for cases where prior methods fail to compute exact answers.","doi":"10.24963/ijcai.2018/186","url":"https://ijcai.org/proceedings/2018/186","pdf":"https://ijcai.org/proceedings/2018/0186.pdf","markdown":"https://ijcai.org/proceedings/2018/186.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/186","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Constraints and SAT","track":"Main track","number":186,"pages":{"first":1339,"last":1345},"keywords":["Constraints and SAT: Constraints and SAT","Constraints and SAT: Constraint Satisfaction","Constraints and SAT: Constraints: Evaluation and Analysis","Constraints and SAT: Constraints: Solvers and Tools"],"videos":[],"references":[{"position":1,"raw":"[Chakraborty et al., 2016] S. 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