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Algorithm for Computing Backbones of Propositional Formulae Based on Solved Backbone Information

机译:基于解决的骨干信息计算命题公式骨干的算法

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The problem of propositional satisfiability (SAT) has found a number of applications in both theoretical and practical computer science. However, in many applications, knowing a formulae's satisfiability alone is not enough to solve problems. Often, some other characteristics of formulae need to be known. In 1997, the definition of backbone occur - a set of variables which are true in any assignment of SAT, which starts the research of solution structure. Gradually, backbone has been recognized having great effect in rapidly solving intelligent planning and automated reasoning problems. First of all, this article reviews three existing algorithms for computing backbone, finding that no algorithm uses solved backbone as information. Based on this idea, we bring up a new algorithm for computing backbone of propositional formulae using solved backbone information. This article utilizes latest SAT competition data on original fastest algorithm (one test per time of 30 chuck) and the new algorithm. According to the experiment results, the new algorithm can have 10% advance on rate at the set of data which contain 80% or more chuck so it has practical application value.
机译:命题可靠性(SAT)的问题在理论和实践计算机科学中发现了许多应用。然而,在许多应用中,了解公式的可靠性仅是解决问题的不足。通常,需要已知一些公式的特征。在1997年,发生了骨干的定义 - 一组变量在饱和的任何转让中都是如此,这开始研究解决方案结构。逐步地,在迅速解决智能规划和自动推理问题方面,骨干被认可很有效果。首先,本文审查了三个用于计算骨干的现有算法,发现没有算法使用已解决的骨干声音作为信息。基于此思想,我们使用Solvave Backbone信息提出了一种用于计算命题公式的骨干的新算法。本文利用原始最快算法的最新SAT竞争数据(每次30个Chuck的一次测试)和新算法。根据实验结果,新算法可以在含有80%或更多卡盘的数据集中进行10%的速率,以便它具有实际应用值。

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