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Pool Resolution and Its Relation to Regular Resolution and DPLL with Clause Learning

机译:子句学习的池分辨率及其与常规分辨率和DPLL的关系

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Pool Resolution for propositional CNF formulas is introduced. Its relationship to state-of-the-art satisfiability solvers is explained. Every regular-resolution derivation is also a pool-resolution derivation. It is shown that a certain family of formulas, called NT~(**)(n) has polynomial sized pool-resolution refutations, whereas the shortest regular refutations have an exponential lower bound. This family is a variant of the GT(n) family analyzed by Bonet and Galesi (FOCS 1999), and the GT'(n) family shown to require exponential-length regular-resolution refutations by Alekhnovitch, Johannsen, Pitassi and Urquhart (STOC 2002). Thus, Pool Resolution is exponentially stronger than Regular Resolution. Roughly speaking a general-resolution derivation is a pool-resolution derivation if its directed acyclic graph (DAG) has a depth-first search tree that satisfies the regularity restriction: on any path in this tree no resolution variable is repeated. In other words, once a clause is derived at a node and used by its tree parent, its derivation is forgotten, and subsequent uses of that clause treat it as though it were an input clause. This policy is closely related to DPLL search with recording of so-called conflict clauses. Variations of DPLL plus conflict analysis currently dominate the field of high-performance satisfiability solving. The power of Pool Resolution might provide some theoretical explanation for their success.
机译:介绍了命题CNF公式的池分辨率。解释了它与最新的可满足性求解器的关系。每个常规分辨率派生也是池分辨率派生。结果表明,称为NT〜(**)(n)的某些公式族具有多项式大小的池分辨率反驳,而最短的正则反驳具有指数下限。这个家族是Bonet和Galesi分析的GT(n)家族的一个变体(FOCS 1999),而Alekhnovitch,Johannsen,Pitassi和Urquhart(STOC)证明GT'(n)家族需要指数长度的常规分辨率反驳。 2002)。因此,池分辨率比常规分辨率指数强。粗略地说,如果通用分辨率推导的有向无环图(DAG)具有满足规则性限制的深度优先搜索树,则它是池分辨率推导:在此树中的任何路径上都不会重复分辨率变量。换句话说,一旦子句在节点上被派生并由其树的父级使用,则它的派生被遗忘,并且对该子句的后续使用将其视为输入子句。该策略与DPLL搜索密切相关,其中记录了所谓的冲突条款。目前,DPLL的各种变体和冲突分析主导着高性能可满足性解决方案领域。池解析的功能可能为它们的成功提供一些理论解释。

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