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摘要翻译：
最大受限路径一致性(maxRPC)是二元约束的局部一致性，它可以实现比圆弧一致性更强的剪枝。然而，现有的maxRRC算法存在开销和冗余，因为它们可以重复执行许多约束检查而不触发任何值删除。本文提出了提高maxRPC算法性能的技术。其中包括两种数据结构的组合使用，以避免许多冗余的约束检查，以及启发式，以有效地排序和执行某些操作。在此基础上，我们提出了两个密切相关的算法。第一种是最优O(end^3)时间复杂度的maxRPC算法，单独使用时性能良好，但在搜索过程中应用代价较高。第二个近似于maxRPC，具有O(en^2d^4)的时间复杂度，但是在搜索过程中使用具有O(end^4)复杂度的限制版本可以非常有效。两种算法的空间复杂度均为O(ed)。实验结果表明，所得到的方法不断地优于以前的maxRPC算法，并且在许多问题上构成了一个比arc一致性更可行的替代方案。
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英文标题：
《Improving the Performance of maxRPC》
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作者：
Thanasis Balafoutis, Anastasia Paparrizou, Kostas Stergiou and Toby
Walsh
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Max Restricted Path Consistency (maxRPC) is a local consistency for binary constraints that can achieve considerably stronger pruning than arc consistency. However, existing maxRRC algorithms suffer from overheads and redundancies as they can repeatedly perform many constraint checks without triggering any value deletions. In this paper we propose techniques that can boost the performance of maxRPC algorithms. These include the combined use of two data structures to avoid many redundant constraint checks, and heuristics for the efficient ordering and execution of certain operations. Based on these, we propose two closely related algorithms. The first one which is a maxRPC algorithm with optimal O(end^3) time complexity, displays good performance when used stand-alone, but is expensive to apply during search. The second one approximates maxRPC and has O(en^2d^4) time complexity, but a restricted version with O(end^4) complexity can be very efficient when used during search. Both algorithms have O(ed) space complexity. Experimental results demonstrate that the resulting methods constantly outperform previous algorithms for maxRPC, often by large margins, and constitute a more than viable alternative to arc consistency on many problems.
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PDF链接：
https://arxiv.org/pdf/1008.5189
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