Propositional fragments for knowledge compilation and quantified boolean formulae
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A more efficient BDD-based QBF solver
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
A symbolic search based approach for quantified boolean formulas
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
A branching heuristics for quantified renamable horn formulas
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
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Solving Quantified Boolean Formulas (QBF) has become an attractive research area in Artificial intelligence. Many important artificial intelligence problems (planning, non monotonic reasoning, formal verification, etc.) can be reduced to QBFs. In this paper, a new DLL-based method is proposed that integrates binary decision diagram (BDD) to set free the variable ordering heuristics that are traditionally constrained by the static order of the QBF quantifiers. BDD is used to represent in a compact form the set of models of the boolean formula. Interesting reduction operators are proposed in order to dynamically reduce the BDD size and to answer the validity of the QBF. Experimental results on instances from the QBFý03 evaluation show that our approach can efficiently solve instances that are very hard for current QBF solvers.