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A Non-prenex, Non-clausal QBF Solver with Game-State Learning

  • William Klieber
  • Samir Sapra
  • Sicun Gao
  • Edmund Clarke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6175)

Abstract

We describe a DPLL-based solver for the problem of quantified boolean formulas (QBF) in non-prenex, non-CNF form. We make two contributions. First, we reformulate clause/cube learning, extending it to non-prenex instances. We call the resulting technique game-state learning. Second, we introduce a propagation technique using ghost literals that exploits the structure of a non-CNF instance in a manner that is symmetric between the universal and existential variables. Experimental results on the QBFLIB benchmarks indicate our approach outperforms other state-of-the-art solvers on certain benchmark families, including the tipfixpoint and tipdiam families of model checking problems.

Keywords

QBF DPLL non-clausal non-prenex clause learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • William Klieber
    • 1
  • Samir Sapra
    • 1
  • Sicun Gao
    • 1
  • Edmund Clarke
    • 1
  1. 1.Computer Science DepartmentCarnegie Mellon UniversityPittsburghUSA

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