QuBE: A System for Deciding Quantified Boolean Formulas Satisfiability

Giunchiglia, Enrico; Narizzano, Massimo; Tacchella, Armando

doi:10.1007/3-540-45744-5_27

Cited by 86 publications

(63 citation statements)

References 7 publications

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“…All tests were run on a 2.8GHz machine with 12GB of RAM under a time limit of 1200 CPU seconds per instance. Table 1 shows the comparison between CirQit2.1, CirQit2 [3], CirQit [2] and some state-of-the-art CNF-based QBF solvers: quantor (version 3.0, with the recommended picosat back end) [6], Qube (version 6.5) [7], nenofex [8] and depqbf [9]-the latter two are the versions submitted to the main track of QBFEVAL'10. The non-prenex non-CNF instances are converted to prenex CNF using a conversion tool available on the QBFEval site.…”

Section: Resultsmentioning

confidence: 99%

Exploiting Circuit Representations in QBF Solving

Goultiaeva¹,

Bacchus²

2010

Theory and Applications of Satisfiability Testing – SAT 2010

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Abstract.Previous work has shown that circuit representations can be exploited in QBF solvers to obtain useful performance improvements. In this paper we examine some additional techniques for exploiting a circuit representations. We discuss the techniques of propagating a dual set of values through the circuit, conversion from simple negation normal form to a more optimized circuit representation, and adding phase memorization during search. We have implemented these techniques in a new QBF solver called CirQit2 and evaluated their impact experimentally. The solver has also displayed superior performance in the nonprenex non-CNF track of the QBFEval'10 competition.

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Section: Resultsmentioning

confidence: 99%

Exploiting Circuit Representations in QBF Solving

Goultiaeva¹,

Bacchus²

2010

Theory and Applications of Satisfiability Testing – SAT 2010

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“…Among the different tools, there is a propositional theorem-prover, boole, based on binary decision diagrams, 3 a system using a generalised resolution principle [27], several provers implementing an extended Davis-Putnam procedure [4,46,19,24,30], as well as a distributed algorithm running on a PC-cluster [19].…”

Section: Methodsmentioning

confidence: 99%

“…The existence of efficient QBF solvers, such as the systems developed by Cadoli et al [4], Rintanen [46], Feldmann et al [19], or Giunchiglia et al [24], makes such a rapid prototyping approach practicably applicable. A similar approach for solving various reasoning tasks belonging to the area of nonmonotonic reasoning has been realised in the system QUIP [15,41,17].…”

Section: Introductionmentioning

confidence: 99%

On Computing Belief Change Operations using Quantified Boolean Formulas

Delgrande

Schaub

Tompits

et al. 2004

Journal of Logic and Computation

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In this paper, we show how an approach to belief revision and belief contraction can be axiomatised by means of quantified Boolean formulas. Specifically, we consider the approach of belief change scenarios, a general framework that has been introduced for expressing different forms of belief change. The essential idea is that for a belief change scenario (K, R, C), the set of formulas K, representing the knowledge base, is modified so that the sets of formulas R and C are respectively true in, and consistent with the result. By restricting the form of a belief change scenario, one obtains specific belief change operators including belief revision, contraction, update, and merging. For both the general approach and for specific operators, we give a quantified Boolean formula such that satisfying truth assignments to the free variables correspond to belief change extensions in the original approach. Hence, we reduce the problem of determining the results of a belief change operation to that of satisfiability. This approach has several benefits. First, it furnishes an axiomatic specification of belief change with respect to belief change scenarios. This then leads to further insight into the belief change framework.

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“…As we focus on QDPLL with learning [12,22,32,33] (see Sec. 4.1), we confine the theoretical framework in that respect.…”

Section: Dependency Schemes In Theorymentioning

confidence: 99%

“…We focus on searchbased QBF solvers for PCNF which implement the DPLL algorithm for QBF [7] (QDPLL) with learning like [12,32,33]. By considering the main parts of QD-PLL such as boolean constraint propagation, decision making or learning, we show how to integrate dependency DAGs into QDPLL in order to profit from dependency schemes in practice (Sec.…”

Section: Introductionmentioning

confidence: 99%

Integrating Dependency Schemes in Search-Based QBF Solvers

Lonsing

Biere

2010

Theory and Applications of Satisfiability Testing – SAT 2010

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Abstract. Many search-based QBF solvers implementing the DPLL algorithm for QBF (QDPLL) process formulae in prenex conjunctive normal form (PCNF). The quantifier prefix of PCNFs often results in strong variable dependencies which can influence solver performance negatively. A common approach to overcome this problem is to reconstruct quantifier structure e.g. by quantifier trees. Dependency schemes are a generalization of quantifier trees in the sense that more general dependency graphs can be obtained. So far, dependency graphs have not been applied in QBF solving. In this work we consider the problem of efficiently integrating dependency graphs in QDPLL. Thereby we generalize related work on integrating quantifier trees. By analyzing the core parts of QDPLL, we report on modifications necessary to profit from general dependency graphs. In comprehensive experiments we show that QDPLL using a particular dependency graph, despite of increased overhead, outperforms classical QDPLL relying on quantifier prefixes of PCNFs.

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QuBE: A System for Deciding Quantified Boolean Formulas Satisfiability

Cited by 86 publications

References 7 publications

Exploiting Circuit Representations in QBF Solving

Exploiting Circuit Representations in QBF Solving

On Computing Belief Change Operations using Quantified Boolean Formulas

Integrating Dependency Schemes in Search-Based QBF Solvers

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