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Solving CSP Including a Universal Quantification

机译:解决CSP,包括通用量化

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摘要

This paper presents a method to solve constraint satisfaction problems including a universally quantified variable with finite domain. Similar problems appear in the field of bounded model checking. The presented method is built on top of the Mozart constraint programming platform. The main principle of the algorithm is to consider only representative values in the domain of the quantified variable. The presented algorithm is similar to a branch and bound search. Significant improvements have been achieved both in memory consumption and execution time compared to a naive approach.
机译:本文介绍了解决约束满足问题的方法,包括具有有限域的普遍定量变量。 界限模型检查领域出现了类似的问题。 呈现的方法是基于Mozart约束编程平台的顶部。 算法的主要原理是考虑量化变量域中的代表值。 呈现的算法类似于分支和绑定搜索。 与天真的方法相比,在记忆消耗和执行时间内实现了显着的改进。

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