摘要翻译：
由合作规划机构组成的团队实现共同目标需要大量的协调和沟通努力。对于一个在动态环境中面临计划失败的单代理系统，可以说，试图修复失败的计划通常不会直接带来任何时间复杂性方面的好处。然而，在多Agent设置中，通信复杂性可能具有更高的重要性，在某些领域中，高通信开销甚至可能是令人望而却步的。我们假设，在分散的系统中，为了实现联合目标而加强协调，试图修复失败的多智能体计划应该比从头开始重新规划导致更低的通信开销。本文的贡献有三个方面。首先，我们正式地介绍了多Agent计划修复问题，并正式地给出了我们工作的核心假设。其次，提出了三种多Agent计划修复算法，将多Agent计划修复问题归结为多Agent计划修复问题的具体实例。最后，我们给出了实验验证的结果，证实了本文的核心假设。
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英文标题：
《Decentralized Multi-agent Plan Repair in Dynamic Environments》
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作者：
Anton\'in Komenda, Peter Nov\'ak, Michal P\v{e}chou\v{c}ek
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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：Multiagent Systems        多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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英文摘要：
  Achieving joint objectives by teams of cooperative planning agents requires significant coordination and communication efforts. For a single-agent system facing a plan failure in a dynamic environment, arguably, attempts to repair the failed plan in general do not straightforwardly bring any benefit in terms of time complexity. However, in multi-agent settings the communication complexity might be of a much higher importance, possibly a high communication overhead might be even prohibitive in certain domains. We hypothesize that in decentralized systems, where coordination is enforced to achieve joint objectives, attempts to repair failed multi-agent plans should lead to lower communication overhead than replanning from scratch.   The contribution of the presented paper is threefold. Firstly, we formally introduce the multi-agent plan repair problem and formally present the core hypothesis underlying our work. Secondly, we propose three algorithms for multi-agent plan repair reducing the problem to specialized instances of the multi-agent planning problem. Finally, we present results of experimental validation confirming the core hypothesis of the paper.
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PDF链接：
https://arxiv.org/pdf/1202.2773