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.
This work was partly supported by the DFG as part of the AVACS Transregional Collaborative Research Center (SFB/TR 14).
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Becker, B., Ehlers, R., Lewis, M., Marin, P. (2012). ALLQBF Solving by Computational Learning. In: Chakraborty, S., Mukund, M. (eds) Automated Technology for Verification and Analysis. ATVA 2012. Lecture Notes in Computer Science, vol 7561. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33386-6_29
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DOI: https://doi.org/10.1007/978-3-642-33386-6_29
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