Algorithms based on singleton arc consistency (SAC) show considerable promise for improving backtrack search algorithms for constraint satisfaction problems (CSPs). The drawback is that even the most efficient of them is still comparatively expensive. Even when limited to preprocessing, they give overall improvement only when problems are quite difficult to solve with more typical procedures such as maintained arc consistency (MAC). The present work examines a form of partial SAC and neighbourhood SAC (NSAC) in which a subset of the variables in a CSP are chosen to be made SAC-consistent or neighbourhood-SAC-consistent. These consistencies are well-characterized in that algorithms have unique fixpoints and there are well-defined dominance relations. Heuristic strategies for choosing an effective subset of variables are described and tested, in particular a strategy of choosing by constraint weight after random probing. Experimental results justify the claim that these methods can be nearly as effective as full (N)SAC in terms of values discarded while significantly reducing the effort required.

中文翻译：

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约束满足问题的部分（邻域）单例弧一致性

基于单例弧一致性 (SAC) 的算法在改进约束满足问题 (CSP) 的回溯搜索算法方面显示出相当大的前景。缺点是即使是最有效的它们仍然相对昂贵。即使仅限于预处理，它们也只有在使用更典型的程序（例如保持电弧一致性 (MAC)）很难解决问题时才会提供整体改进。目前的工作检查了部分 SAC 和邻域 SAC (NSAC) 的一种形式，其中选择 CSP 中的变量子集使 SAC 一致或邻域 SAC 一致。这些一致性的特征在于算法具有独特的固定点，并且存在明确定义的优势关系。描述和测试了选择有效变量子集的启发式策略，特别是随机探测后通过约束权重进行选择的策略。实验结果证明这些方法在丢弃的值方面几乎与完全 (N)SAC 一样有效，同时显着减少了所需的工作量。