{"title":"基于 CDCL 的 QBF 求解中的依赖方案:证明理论研究","authors":"Abhimanyu Choudhury, Meena Mahajan","doi":"10.1007/s10817-024-09707-4","DOIUrl":null,"url":null,"abstract":"<p>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 <span>\\(\\texttt{QCDCL}\\)</span> 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 <span>\\(\\texttt{D}^{\\texttt{rrs}}\\)</span> is used, a mixed picture emerges: the proof systems that add <span>\\(\\texttt{D}^{\\texttt{rrs}}\\)</span> to <span>\\(\\texttt{QCDCL}\\)</span> in these three ways are not only incomparable with each other, but are also incomparable with the basic <span>\\(\\texttt{QCDCL}\\)</span> proof system that does not use <span>\\(\\texttt{D}^{\\texttt{rrs}}\\)</span> 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.</p>","PeriodicalId":15082,"journal":{"name":"Journal of Automated Reasoning","volume":"47 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dependency Schemes in CDCL-Based QBF Solving: A Proof-Theoretic Study\",\"authors\":\"Abhimanyu Choudhury, Meena Mahajan\",\"doi\":\"10.1007/s10817-024-09707-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 <span>\\\\(\\\\texttt{QCDCL}\\\\)</span> 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 <span>\\\\(\\\\texttt{D}^{\\\\texttt{rrs}}\\\\)</span> is used, a mixed picture emerges: the proof systems that add <span>\\\\(\\\\texttt{D}^{\\\\texttt{rrs}}\\\\)</span> to <span>\\\\(\\\\texttt{QCDCL}\\\\)</span> in these three ways are not only incomparable with each other, but are also incomparable with the basic <span>\\\\(\\\\texttt{QCDCL}\\\\)</span> proof system that does not use <span>\\\\(\\\\texttt{D}^{\\\\texttt{rrs}}\\\\)</span> 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.</p>\",\"PeriodicalId\":15082,\"journal\":{\"name\":\"Journal of Automated Reasoning\",\"volume\":\"47 1\",\"pages\":\"\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Automated Reasoning\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10817-024-09707-4\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Automated Reasoning","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10817-024-09707-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.