Mining-based compression approach of propositional formulae

Saïd Jabbour, L. Sais, Y. Salhi, T. Uno
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引用次数: 8

Abstract

In this paper, we propose a first application of data mining techniques to propositional satisfiability. Our proposed mining based compression approach aims to discover and to exploit hidden structural knowledge for reducing the size of propositional formulae in conjunctive normal form (CNF). It combines both frequent itemset mining techniques and Tseitin's encoding for a compact representation of CNF formulae. The experimental evaluation of our approach shows interesting reductions of the sizes of many application instances taken from the last SAT competitions.
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基于挖掘的命题公式压缩方法
在本文中,我们提出了数据挖掘技术在命题可满足性中的第一个应用。我们提出的基于挖掘的压缩方法旨在发现和利用隐藏的结构知识,以减少合取范式(CNF)的命题公式的大小。它结合了频繁项集挖掘技术和tseittin的编码,用于压缩CNF公式的表示。对我们方法的实验评估显示,从上次SAT竞赛中获得的许多应用程序实例的大小有趣地减少了。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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