arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1908.00860cs.LO
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SAT模理论对称破缺的研究进展

Advances in Symmetry Breaking for SAT Modulo Theories

Saket Dingliwal, Ronak Agarwal, Happy Mittal, Parag Singla

摘要:

对称破缺是一种常用的技术,它利用公式中变量和子句的对称性来减少SAT求解的搜索空间。其关键思想是首先确定属于同一对称类的赋值集,然后施加排序约束,称为对称性破坏谓词(SBP),使得这些赋值中只有一个(或一个小子集)被允许是原始SAT公式的解。虽然这项技术已被广泛利用在SAT文献,有很少的工作使用对称性破缺SAT模理论(SMT)。在SMT中,SAT理论中的逻辑约束与定义在非布尔变量(如整数、实数等)上的另一组理论运算相结合。SMT求解器通常使用SAT求解技术的组合,并通过调用理论求解器来增强。在这项工作中,我们采取了SAT对称性破缺的进展,并将其应用到域的SMT。我们的关键技术贡献是制定对称性破缺的布尔骨架变量,这是占位符的实际理论操作SMT解决。然后,这些SBP应用于SMT求解器的SAT求解部分。我们在最先进的SMT求解器CVC 4之上实现SBP理念。与最先进的方法相比,我们的方法可以在几个基准问题上获得更快的解决方案。我们的最终求解器是原始CVC 4求解器和基于SBP的求解器的混合体,与该类别中表现最好的CVC 4相比,在2018年和2019年SMT基准的QF_NIA类别中,分别可以解决高达3.8%和3.1%的问题。

英文摘要:

Symmetry breaking is a popular technique to reduce the search space for SAT solving by exploiting the underlying symmetry over variables and clauses in a formula. The key idea is to first identify sets of assignments which fall in the same symmetry class, and then impose ordering constraints, called Symmetry Breaking Predicates (SBPs), such that only one (or a small subset) of these assignments is allowed to be a solution of the original SAT formula. While this technique has been exploited extensively in the SAT literature, there is little work on using symmetry breaking for SAT Modulo Theories (SMT). In SMT, logical constraints in SAT theories are combined with another set of theory operations defined over non-Boolean variables such as integers, reals, etc. SMT solvers typically use a combination of SAT solving techniques augmented with calls to the theory solver. In this work, we take up the advances in SAT symmetry breaking and apply them to the domain of SMT. Our key technical contribution is the formulation of symmetry breaking over the Boolean skeleton variables, which are placeholders for actual theory operations in SMT solving. These SBPs are then applied over the SAT solving part of the SMT solver. We implement our SBP ideas on top of CVC4, which is a state-of-the-art SMT solver. Our approach can result in significantly faster solutions on several benchmark problems compared to the state-of-the-art. Our final solver is a hybrid of the original CVC4 solver, and an SBP based solver, and can solve up to 3.8% and 3.1% more problems in the QF_NIA category of 2018 and 2019 SMT benchmarks, respectively, compared to CVC4, the top performer in this category.

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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