Solving Stochastic Boolean Satisfiability under Random-Exist Quantification
Journal
International Joint Conference on Artificial Intelligence (IJCAI)
Part Of
IJCAI International Joint Conference on Artificial Intelligence
Pages
797
Start Page
688
End Page
694
ISBN (of the container)
978-099924110-3
Date Issued
2017
Author(s)
Abstract
Stochastic Boolean Satisfiability (SSAT) is a powerful formalism to represent computational problems with uncertainly, such as belief network inference and propositional probabilistic planning. Solving SSAT formulas lies in the same complexity class (PSPACE-complete) as solving Quantified Boolean Formula (QBF). While many endeavors have been made to enhance QBF solving, SSAT has drawn relatively less attention in recent years. This paper focuses on random-exist quantified SSAT formulas, and proposes an algorithm combining binary decision diagram (BDD), logic synthesis, and modern SAT techniques to improve computational efficiency. Unlike prior exact SSAT algorithms, the proposed method can be easily modified to solve approximate SSAT by deriving upper and lower bounds of satisfying probability. Experimental results show that our method outperforms the state-of-the-art algorithm on random k-CNF formulas and has effective application to approximate SSAT on circuit benchmarks.
Event(s)
26th International Joint Conference on Artificial Intelligence, IJCAI 2017
SDGs
Type
conference paper