摘要翻译：
鲁棒搜索过程是黑箱约束规划求解器设计的核心组成部分。本文提出了基于活动的搜索，利用变量在传播过程中的活动性来指导搜索的思想。将基于活动的搜索与基于影响的搜索和WDEG启发式进行了实验比较。在各种基准上的实验结果表明，基于活动的搜索比其他启发式搜索具有更强的鲁棒性，并可能在性能上产生显著的改善。
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英文标题：
《Activity-Based Search for Black-Box Contraint-Programming Solvers》
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作者：
L. Michel and P. Van Hentenryck
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最新提交年份：
2011
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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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一级分类：Computer Science        计算机科学
二级分类：Mathematical Software        数学软件
分类描述：Roughly includes material in ACM Subject Class G.4.
大致包括ACM学科类G.4的材料。
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英文摘要：
  Robust search procedures are a central component in the design of black-box constraint-programming solvers. This paper proposes activity-based search, the idea of using the activity of variables during propagation to guide the search. Activity-based search was compared experimentally to impact-based search and the WDEG heuristics. Experimental results on a variety of benchmarks show that activity-based search is more robust than other heuristics and may produce significant improvements in performance.
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PDF链接：
https://arxiv.org/pdf/1105.6314