Communications of the ACM
Resolution for quantified Boolean formulas
Information and Computation
Exact learning Boolean functions via the monotone theory
Information and Computation
Machine Learning
Machine Learning
Symbolic Model Checking without BDDs
TACAS '99 Proceedings of the 5th International Conference on Tools and Algorithms for Construction and Analysis of Systems
Applying SAT Methods in Unbounded Symbolic Model Checking
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Exact learning of DNF formulas using DNF hypotheses
Journal of Computer and System Sciences - Special issue on COLT 2002
An Antichain Algorithm for LTL Realizability
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Clause/term resolution and learning in the evaluation of quantified Boolean formulas
Journal of Artificial Intelligence Research
Unbeast: symbolic bounded synthesis
TACAS'11/ETAPS'11 Proceedings of the 17th international conference on Tools and algorithms for the construction and analysis of systems: part of the joint European conferences on theory and practice of software
Existential quantification as incremental SAT
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
sQueezeBF: an effective preprocessor for QBFs based on equivalence reasoning
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Inferring definite counterexamples through under-approximation
NFM'12 Proceedings of the 4th international conference on NASA Formal Methods
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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.