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Solving QBF with Counterexample Guided Refinement

  • Mikoláš Janota
  • William Klieber
  • Joao Marques-Silva
  • Edmund Clarke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7317)

Abstract

We propose two novel approaches for using Counterexample-Guided 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.

Keywords

Model Check Conjunctive Normal Form Recursive Call Winning Strategy Boolean Formula 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mikoláš Janota
    • 1
  • William Klieber
    • 3
  • Joao Marques-Silva
    • 1
    • 2
  • Edmund Clarke
    • 3
  1. 1.IST/INESC-IDLisbonPortugal
  2. 2.University College DublinIreland
  3. 3.Carnegie Mellon UniversityPittsburghUSA

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