{"type":"ScholarlyArticle","title":"Knowledge Compilation for Incremental and Checkable Stochastic Boolean Satisfiability","authors":[{"name":"Che Cheng","slug":"che-cheng","given":"Che","family":"Cheng","affiliation":"Graduate Institute of Electronics Engineering , National Taiwan University , Taipei , Taiwan"},{"name":"Yun-Rong Luo","slug":"yun-rong-luo","given":"Yun-Rong","family":"Luo","affiliation":"University of Michigan , Ann Arbor MI , USA"},{"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":"Knowledge compilation has proven effective in (weighted) model counting, uniquely supporting incrementality and checkability. For incrementality, compiling an input formula once suffices to answer multiple queries, thus reducing the total solving effort. For checkability, the compiled formula is amenable to producing machine-checkable proofs for verification, thus strengthening the solver’s reliability. In this work, we extend knowledge compilation from model counting to stochastic Boolean satisfiability (SSAT) solving by generalizing the dec-DNNF representation to accommodate the SSAT quantifier structure and integrate it into SharpSSAT, a state-of-the-art SSAT solver. We further study proof generation from the compiled representation and extend CPOG, a certified model-counting toolchain, to generate proofs for certifying the results of SharpSSAT. Experimental results show the benefits of the proposed knowledge compilation approach for SSAT in sharing computation efforts for multiple queries and producing checkable dec-DNNF logs with negligible overhead.","doi":"10.24963/ijcai.2024/206","url":"https://ijcai.org/proceedings/2024/206","pdf":"https://ijcai.org/proceedings/2024/0206.pdf","markdown":"https://ijcai.org/proceedings/2024/206.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/206","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Constraint Satisfaction and Optimization","track":"Main Track","number":206,"pages":{"first":1862,"last":1872},"keywords":["Knowledge Representation and Reasoning: KRR: Knowledge compilation","Constraint Satisfaction and Optimization: CSO: Satisfiabilty","Constraint Satisfaction and Optimization: CSO: Solvers and tools","Constraint Satisfaction and Optimization: CSO: Constraint satisfaction"],"videos":[],"references":[{"position":1,"raw":"[Bryant et al., 2023] Randal E. 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