摘要翻译：
决策理论故障排除是关于最小化解决某个问题的预期成本，比如修复一个复杂的人造设备。在本文中，我们考虑的情况是，您必须拆开一些设备才能访问某些集群和操作。具体地说，我们研究了集群树中独立操作的故障排除，其中在集群打开之前，集群内的操作无法执行。这个问题并不简单，因为打开和关闭群集会有相关的成本。Kadane和Simon提出的“P-over-C”算法可以在O（n lg n）时间内（n为动作个数）解决具有独立动作且无簇的故障诊断问题，但对于树型簇模型，还没有找到一个高效的最优算法。本文描述了一个“自底向上P-over-C”O(n，lg，n)时间算法，并证明了当不需要关闭簇来测试动作是否解决问题时，该算法是最优的。
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英文标题：
《The Cost of Troubleshooting Cost Clusters with Inside Information》
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作者：
Thorsten J. Ottosen, Finn Verner Jensen
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最新提交年份：
2012
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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：Data Structures and Algorithms        数据结构与算法
分类描述：Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
涵盖数据结构和算法分析。大致包括ACM学科类E.1、E.2、F.2.1和F.2.2中的材料。
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英文摘要：
  Decision theoretical troubleshooting is about minimizing the expected cost of solving a certain problem like repairing a complicated man-made device. In this paper we consider situations where you have to take apart some of the device to get access to certain clusters and actions. Specifically, we investigate troubleshooting with independent actions in a tree of clusters where actions inside a cluster cannot be performed before the cluster is opened. The problem is non-trivial because there is a cost associated with opening and closing a cluster. Troubleshooting with independent actions and no clusters can be solved in O(n lg n) time (n being the number of actions) by the well-known "P-over-C" algorithm due to Kadane and Simon, but an efficient and optimal algorithm for a tree cluster model has not yet been found. In this paper we describe a "bottom-up P-over-C" O(n lg n) time algorithm and show that it is optimal when the clusters do not need to be closed to test whether the actions solved the problem.
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PDF链接：
https://arxiv.org/pdf/1203.3502