Abstract. Quantified Boolean formulas (QBF) provide a powerful framework for encoding problems from various application domains, not least because efficient QBF solvers are available. Despite sophisticated evaluation techniques, the performance of such a solver usually depends on the way a problem is represented. However, the translation to processable QBF encodings is in general not unique and may either introduce variables and clauses not relevant for the solving process or blur information which could be beneficial for the solving process. To deal with both of these issues, preprocessors have been introduced which rewrite a given QBF before it is passed to a solver. In this paper, we present novel preprocessing methods for QBF based on blocked clause elimination (BCE), a technique successfully applied in SAT. Quantified blocked clause elimination (QBCE) allows to simulate various structural preprocessing techniques as BCE in SAT. We have implemented QBCE and extensions of QBCE in the preprocessor bloqqer.In our experiments we show that preprocessing with QBCE reduces formulas substantially and allows us to solve considerable more instances than the previous state-of-the-art.

The famous archetypical NP-complete problem of Boolean satisfiability (SAT) and its PSPACE-complete generalization of quantified Boolean satisfiability (QSAT) have become central declarative programming paradigms through which real-world instances of various computationally hard problems can be efficiently solved. This success has been achieved through several breakthroughs in practical implementations of decision procedures for SAT and QSAT, that is, in SAT and QSAT solvers. Here, simplification techniques for conjunctive normal form (CNF) for SAT and for prenex conjunctive normal form (PCNF) for QSAT---the standard input formats of SAT and QSAT solvers---have recently proven very effective in increasing solver efficiency when applied before (i.e., in preprocessing) or during (i.e., in inprocessing) satisfiability search. In this article, we develop and analyze clause elimination procedures for pre- and inprocessing. Clause elimination procedures form a family of (P)CNF formula simplification techniques which remove clauses that have specific (in practice polynomial-time) redundancy properties while maintaining the satisfiability status of the formulas. Extending known procedures such as tautology, subsumption, and blocked clause elimination, we introduce novel elimination procedures based on asymmetric variants of these techniques, and also develop a novel family of so-called covered clause elimination procedures, as well as natural liftings of the CNF-level procedures to PCNF. We analyze the considered clause elimination procedures from various perspectives. Furthermore, for the variants not preserving logical equivalence under clause elimination, we show how to reconstruct solutions to original CNFs from satisfying assignments to simplified CNFs, which is important for practical applications for the procedures. Complementing the more theoretical analysis, we present results on an empirical evaluation on the practical importance of the clause elimination procedures in terms of the effect on solver runtimes on standard real-world application benchmarks. It turns out that the importance of applying the clause elimination procedures developed in this work is empirically emphasized in the context of state-of-the-art QSAT solving.

We present DepQBF 0.1, a new search-based solver for quantified boolean formulae (QBF). It integrates compact dependency graphs to overcome the restrictions imposed by linear quantifier prefixes of QBFs in prenex conjunctive normal form (PCNF). DepQBF 0.1 was placed first in the main track of QBFEVAL'10 in a score-based ranking. We provide a general system overview and describe selected orthogonal features such as restarts and removal of learnt constraints.

Abstract. Robustness and correctness are essential criteria for SAT and QBF solvers. We develop automated testing and debugging techniques designed and optimized for SAT and QBF solver development. Our fuzz testing techniques are able to find critical solver defects that lead to crashes, invalid satisfying assignments and incorrect satisfiability results. Moreover, we show that sequential and concurrent delta debugging techniques are highly effective in minimizing failure-inducing inputs.

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