A non-prenex, non-clausal QBF solver with game-state learning

Authors:
William Klieber;Samir Sapra;Sicun Gao;Edmund Clarke
Affiliations:
Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania;Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania;Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania;Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania
Venue:
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Year:
2010

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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.