Solving QBF with counterexample guided refinement

Abstract

We propose two novel approaches for using CounterexampleGuided Abstraction Refinement (CEGAR) in Quantified Boolean Formula (QBF) solvers. The first approach develops a recursive algorithm whose search is driven by CEGAR (rather than by DPLL). The second approach employs CEGAR as an additional learning technique in an existing DPLL-based QBF solver. Experimental evaluation of the implemented prototypes shows that the CEGAR-driven solver outperforms existing solvers on a number of families in the QBF-LIB and that the DPLL solver benefits from the additional type of learning. Thus this article opens two promising avenues in QBF: CEGAR-driven solvers as an alternative to existing approaches and a novel type of learning in DPLL.

DOI: 10.1016/j.artint.2016.01.004
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@inproceedings{Janota2012SolvingQW, title={Solving QBF with counterexample guided refinement}, author={Mikol{\'a}s Janota and William Klieber and Joao Marques-Silva and Edmund M. Clarke}, booktitle={Artif. Intell.}, year={2012} }