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- W4382240109 abstract "Stochastic Boolean satisfiability (SSAT) is a formalism allowing decision-making for optimization under quantitative constraints. Although SSAT solvers are under active development, existing solvers do not provide Skolem-function witnesses, which are crucial for practical applications. In this work, we develop a new witness-generating SSAT solver, SharpSSAT, which integrates techniques, including component caching, clause learning, and pure literal detection. It can generate a set of Skolem functions witnessing the attained satisfying probability of a given SSAT formula. We also equip the solver ClauSSat with witness generation capability for comparison. Experimental results show that SharpSSAT outperforms current state-of-the-art solvers and can effectively generate compact Skolem-function witnesses. The new witness-generating solver may broaden the applicability of SSAT to practical applications." @default.
- W4382240109 created "2023-06-28" @default.
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- W4382240109 date "2023-06-26" @default.
- W4382240109 modified "2023-10-16" @default.
- W4382240109 title "SharpSSAT: A Witness-Generating Stochastic Boolean Satisfiability Solver" @default.
- W4382240109 doi "https://doi.org/10.1609/aaai.v37i4.25509" @default.
- W4382240109 hasPublicationYear "2023" @default.
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