The complexity of logic-based abduction
Journal of the ACM (JACM)
An Algorithm to Evaluate Quantified Boolean Formulae and Its Experimental Evaluation
Journal of Automated Reasoning
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Towards a Symmetric Treatment of Satisfaction and Conflicts in Quantified Boolean Formula Evaluation
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
SATO: An Efficient Propositional Prover
CADE-14 Proceedings of the 14th International Conference on Automated Deduction
Conflict driven learning in a quantified Boolean Satisfiability solver
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Learning for quantified boolean logic satisfiability
Eighteenth national conference on Artificial intelligence
Backjumping for quantified Boolean logic satisfiability
Artificial Intelligence
Design of Logic-based Intelligent Systems
Design of Logic-based Intelligent Systems
An Effective Algorithm for the Futile Questioning Problem
Journal of Automated Reasoning
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
Abstraction-based algorithm for 2QBF
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Solving QBF with counterexample guided refinement
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
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Although the satisfiability problem (SAT) is NP-complete, state-of-the-art solvers for SAT can solve instances that are considered to be very hard. Emerging applications demand to solve even more complex problems residing at the second or higher levels of the polynomial hierarchy. We identify such a problem, called Q-ALL SAT, that arises in a variety of applications. We have designed a solution algorithm for Q-ALL SAT that employs a SAT solver and thus exploits the recent advances of SAT solvers. In addition, a heuristic is applied to reduce the number of instances that are to be solved by the SAT solver. A learning scheme improves the performance of that heuristic. Test results of a first implementation of the proposed algorithm confirm that this is a very promising approach.