基于 CDCL 的 QBF 求解中的依赖方案:证明理论研究

IF 0.9 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Abhimanyu Choudhury, Meena Mahajan
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引用次数: 0

摘要

在量化布尔公式 QBF 中,依赖性方案有助于检测虚假或多余的依赖性,这些依赖性由量词前缀中的变量排序所隐含,但对于构建反模型并不重要。这种检测可以缩短特定证明系统中的反驳时间,并有望加快 QBF 求解器的运行速度。贝耶斯多夫(Beyersdorff)和博姆(Boehm)(LMCS 2023)最近定义的证明系统(\texttt{QCDCL}\)抽象了基于冲突驱动子句学习(CDCL)技术的QBF求解器所使用的推理。我们展示了如何在预处理阶段、传播和子句学习阶段,或在这两个阶段,将依赖关系方案的使用纳入该证明系统。然后我们展示了当使用反向解析路径依赖方案(\texttt{D}^{texttt{rrs}}\)时,会出现一种混合的情况:以这三种方式在\(texttt{D}^{texttt{rs}}\)中添加\(texttt{QCDCL}}\)的证明系统不仅是不可比的,而且与完全不使用\(texttt{D}^{texttt{rs}}\)的基本\(texttt{QCDCL}}\)证明系统以及其他几个基于解析的QBF证明系统也是不可比的。一个值得注意的事实是,我们所有的分离都是通过有界量词交替的 QBF 实现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Dependency Schemes in CDCL-Based QBF Solving: A Proof-Theoretic Study

Dependency Schemes in CDCL-Based QBF Solving: A Proof-Theoretic Study

In Quantified Boolean Formulas QBFs, dependency schemes help to detect spurious or superfluous dependencies that are implied by the variable ordering in the quantifier prefix but are not essential for constructing countermodels. This detection can provably shorten refutations in specific proof systems, and is expected to speed up runs of QBF solvers. The proof system \(\texttt{QCDCL}\) recently defined by Beyersdorff and Boehm (LMCS 2023) abstracts the reasoning employed by QBF solvers based on conflict-driven clause-learning (CDCL) techniques. We show how to incorporate the use of dependency schemes into this proof system, either in a preprocessing phase, or in the propagations and clause learning, or both. We then show that when the reflexive resolution path dependency scheme \(\texttt{D}^{\texttt{rrs}}\) is used, a mixed picture emerges: the proof systems that add \(\texttt{D}^{\texttt{rrs}}\) to \(\texttt{QCDCL}\) in these three ways are not only incomparable with each other, but are also incomparable with the basic \(\texttt{QCDCL}\) proof system that does not use \(\texttt{D}^{\texttt{rrs}}\) at all, as well as with several other resolution-based QBF proof systems. A notable fact is that all our separations are achieved through QBFs with bounded quantifier alternation.

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来源期刊
Journal of Automated Reasoning
Journal of Automated Reasoning 工程技术-计算机:人工智能
CiteScore
3.60
自引率
9.10%
发文量
31
审稿时长
>12 weeks
期刊介绍: The Journal of Automated Reasoning is an interdisciplinary journal that maintains a balance between theory, implementation and application. The spectrum of material published ranges from the presentation of a new inference rule with proof of its logical properties to a detailed account of a computer program designed to solve various problems in industry. The main fields covered are automated theorem proving, logic programming, expert systems, program synthesis and validation, artificial intelligence, computational logic, robotics, and various industrial applications. The papers share the common feature of focusing on several aspects of automated reasoning, a field whose objective is the design and implementation of a computer program that serves as an assistant in solving problems and in answering questions that require reasoning. The Journal of Automated Reasoning provides a forum and a means for exchanging information for those interested purely in theory, those interested primarily in implementation, and those interested in specific research and industrial applications.
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