Quantified Boolean Formulas

Olaf Beyersdorff, Mikoláš Janota, Florian Lonsing, M. Seidl
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引用次数: 19

Abstract

Solvers for quantified Boolean formulas (QBF) have become powerful tools for tackling hard computational problems from various application domains, even beyond the scope of SAT. This chapter gives a description of the main algorithmic paradigms for QBF solving, including quantified conflict driven clause learning (QCDCL), expansion-based solving, dependency schemes, and QBF preprocessing. Particular emphasis is laid on the connections of these solving approaches to QBF proof systems: Q-Resolution and its variants in the case of QCDCL, expansion QBF resolution calculi for expansion-based solving, and QRAT for preprocessing. The chapter also surveys the relations between the various QBF proof systems and results on their proof complexity, thereby shedding light on the diverse performance characteristics of different solving approaches that are observed in practice.
量化布尔公式
量化布尔公式(QBF)的求解器已经成为解决来自各种应用领域的困难计算问题的强大工具,甚至超出了SAT的范围。本章描述了求解QBF的主要算法范例,包括量化冲突驱动子句学习(QCDCL)、基于扩展的求解、依赖方案和QBF预处理。特别强调了这些求解QBF证明系统的方法之间的联系:QCDCL中的Q-Resolution及其变体,基于展开式求解的扩展QBF resolution演算,以及用于预处理的QRAT。本章还调查了各种QBF证明系统及其证明复杂性结果之间的关系,从而揭示了在实践中观察到的不同求解方法的不同性能特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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