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arXiv 2303.14971cs.LO
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关于 SAT 和 SMT 枚举的 CNF 转换

On CNF Conversion for SAT and SMT Enumeration

Gabriele Masina, Giuseppe Spallitta, Roberto Sebastiani

AI总结:

本文从理论和实验两方面研究了 CNF 转换对 SAT 和 SMT 枚举的影响,发现将 Plaisted and Greenbaum 转换与 NNF 预处理结合可完全克服部分赋值过长的问题,大幅减少赋值数量与执行时间。

摘要:

现代 SAT 和 SMT 求解器被设计用于处理以合取范式(CNF)表达的问题,因此非 CNF 问题必须预先进行 CNF 化,通常使用 Tseitin 或 Plaisted and Greenbaum 转换的变体。然而,当从普通求解转向枚举时,生成尽可能小的部分满足赋值的能力变得至关重要,这就引出了此类 CNF 编码是否同样适用于枚举的问题。在本文中,我们从理论和经验两方面研究了 CNF 转换对 SAT 和 SMT 枚举的有效性。在负面结果方面,我们表明:(i) Tseitin 转换阻止求解器生成简短的部分赋值,从而严重影响枚举的有效性;(ii) Plaisted and Greenbaum 转换仅部分解决了此问题。在正面结果方面,我们在理论上证明并在经验上表明,将 Plaisted and Greenbaum 转换与预先的 NNF 预处理(通常不用于求解中)相结合,能够完全克服该问题,并大幅减少部分赋值的数量和执行时间。

英文摘要:

Modern SAT and SMT solvers are designed to handle problems expressed in Conjunctive Normal Form (CNF) so that non-CNF problems must be CNF-ized upfront, typically by using variants of either Tseitin or Plaisted and Greenbaum transformations. When passing from plain solving to enumeration, however, the capability of producing partial satisfying assignments that are as small as possible becomes crucial, which raises the question of whether such CNF encodings are also effective for enumeration. In this paper, we investigate both theoretically and empirically the effectiveness of CNF conversions for SAT and SMT enumeration. On the negative side, we show that: (i) Tseitin transformation prevents the solver from producing short partial assignments, thus seriously affecting the effectiveness of enumeration; (ii) Plaisted and Greenbaum transformation overcomes this problem only in part. On the positive side, we prove theoretically and we show empirically that combining Plaisted and Greenbaum transformation with NNF preprocessing upfront -- which is typically not used in solving -- can fully overcome the problem and can drastically reduce both the number of partial assignments and the execution time.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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