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• W143701725 abstract "In the last years, search-based QBF solvers have become essential for many applications in the formal methods domain. The exploitation of their reasoning efficiency has however been restricted to applications in which a “satisfiable/unsatisfiable” answer or one model of an open quantified Boolean formula suffices as an outcome, whereas applications in which a compact representation of all models is required could not be tackled with QBF solvers so far. In this paper, we describe how computational learning provides a remedy to this problem. Our algorithms employ a search-based QBF solver and learn the set of all models of a given open QBF problem in a CNF (conjunctive normal form), DNF (disjunctive normal form), or CDNF (conjunction of DNFs) representation. We evaluate our approach experimentally using benchmarks from synthesis of finite-state systems from temporal logic and monitor computation." @default.
• W143701725 created "2016-06-24" @default.
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• W143701725 date "2012-01-01" @default.
• W143701725 modified "2023-10-18" @default.
• W143701725 title "ALLQBF Solving by Computational Learning" @default.
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• W143701725 doi "https://doi.org/10.1007/978-3-642-33386-6_29" @default.
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