Peitl Tomáš, Slivovsky Friedrich, Szeider Stefan
Algorithms and Complexity Group, TU Wien, 1040 Vienna, Austria.
J Autom Reason. 2019;63(1):127-155. doi: 10.1007/s10817-018-9467-3. Epub 2018 Jun 9.
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 system-which we call long-distance Q(D)-resolution-is 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.
量化布尔公式(QBF)的归结证明系统为研究使用这些系统生成证明的基于搜索的最先进QBF求解器的局限性提供了一个形式模型。我们研究了求解器DepQBF支持的两个证明系统的组合:根据依赖方案进行广义全称归约的Q归结和长距离Q归结。我们表明,由此产生的证明系统——我们称之为长距离Q(D)归结——对于自反归结路径依赖方案是可靠的。事实上,我们证明它允许在多项式时间内进行策略提取。这是一个一般结果的应用,通过这个结果,我们确定了一类依赖方案,对于这类方案,长距离Q(D)归结允许多项式时间策略提取。作为一个特殊情况,我们得到了具有标准依赖方案的长距离Q(D)归结的可靠性和多项式时间策略提取。我们进一步表明,使用依赖方案D并通过长距离Q归结进行学习的基于搜索的QBF求解器会生成长距离Q(D)归结证明。因此,上述可靠性结果转化为这类求解器的部分可靠性结果:它们仅在输入QBF确实为假时才声明其为假。最后,我们报告了使用标准依赖方案和基于长距离Q归结的学习的DepQBF配置的实验情况。