Clause/term resolution and learning in the evaluation of quantified boolean formulas

被引：0

作者：

Giunchiglia, Enrico ^{[1
]}

Narizzano, Massimo ^{[1
]}

Tacchella, Armando ^{[1
]}

机构：

[1] DIST, Università di Genova, Viale Causa 13, 16145 Genova, Italy

来源：

Journal of Artificial Intelligence Research | 2006年 / 26卷

关键词：

Resolution is the rule of inference at the basis of most procedures for automated reasoning. In these procedures; the input formula is first translated into an equisatisfiable formula in conjunctive normal form (CNF) and then represented as a set of clauses. Deduction starts by inferring new clauses by resolution; and goes on until the empty clause is generated or satisfiability of the set of clauses is proven; e.g; because no new clauses can be generated. In this paper; we restrict our attention to the problem of evaluating Quantified Boolean Formulas (QBFs). In this setting; the above outlined deduction process is known to be sound and complete if given a formula in CNF and if a form of resolution; called Qresolution; is used. We introduce Q-resolution on terms; to be used for formulas in disjunctive normal form. We show that the computation performed by most of the available procedures for QBFs -based on the Davis-Logemann-Loveland procedure (DLL) for propositional satisfiability- corresponds to a tree in which Q-resolution on terms and clauses alternate. This poses the theoretical bases for the introduction of learning; corresponding to recording Q-resolution formulas associated with the nodes of the tree. We discuss the problems related to the introduction of learning in DLL based procedures; and present solutions extending state-of-the-art proposals coming from the literature on propositional satisfiability. Finally; we show that our DLL based solver extended with learning; performs significantly better on benchmarks used in the 2003 QBF solvers comparative evaluation. © 2006 AI Access Foundation. All rights reserved;

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摘要：

Journal article (JA)

引用

页码：371 / 416

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