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首页> 外文期刊>Journal of Automated Reasoning >Long-Distance Q-Resolution with Dependency Schemes
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Long-Distance Q-Resolution with Dependency Schemes

机译:依赖方案的远距离Q解析

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Resolution proof systems for quantified Boolean formulas (QBFs) provide a formal model for studying the limitations of state-of-the-art search-based QBF solvers that use these systems to generate proofs. We study a combination of two proof systems supported by the solver DepQBF: Q-resolution with generalized universal reduction according to a dependency scheme and long distance Q-resolution. We show that the resulting proof systemwhich we call long-distance Q(D)-resolutionis sound for the reflexive resolution-path dependency scheme. In fact, we prove that it admits strategy extraction in polynomial time. This comes as an application of a general result, by which we identify a whole class of dependency schemes for which long-distance Q(D)-resolution admits polynomial-time strategy extraction. As a special case, we obtain soundness and polynomial-time strategy extraction for long distance Q(D)-resolution with the standard dependency scheme. We further show that search-based QBF solvers using a dependency scheme D and learning with long-distance Q-resolution generate long-distance Q(D)-resolution proofs. The above soundness results thus translate to partial soundness results for such solvers: they declare an input QBF to be false only if it is indeed false. Finally, we report on experiments with a configuration of DepQBF that uses the standard dependency scheme and learning based on long-distance Q-resolution.
机译:量化布尔公式(QBF)的分辨率证明系统提供了一个正式的模型,用于研究使用这些系统生成证明的基于搜索的最新QBF求解器的局限性。我们研究了由DepQBF求解器支持的两个证明系统的组合:Q分辨率和根据依赖性方案的广义通用归约和长距离Q分辨率。我们证明了生成的证明系统,我们称之为长距离Q(D)-分辨率,对于自反分辨率-路径相关性方案来说是合理的。实际上,我们证明了它允许多项式时间内的策略提取。这是对一般结果的一种应用,通过该结果,我们可以确定一类依赖项方案,对于这些依赖项方案,长距离Q(D)分辨率允许多项式时间策略提取。作为一种特殊情况,我们使用标准相关性方案来获得长距离Q(D)分辨率的稳健性和多项式时间策略提取。我们进一步证明,使用依赖方案D并基于远程Q分辨率学习的基于搜索的QBF求解器会生成远程Q(D)分辨率证明。因此,上述健全性结果转化为此类求解器的部分健全性结果:仅当输入QBF确实为假时,他们才声明输入QBF为假。最后,我们报告了使用DepQBF的配置进行的实验,该配置使用标准的依存关系方案,并基于远程Q分辨率进行学习​​。

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