On the Design of Social Diagnosis Algorithms for Multi-Agent Teams
On the Design of Social Diagnosis Algorithms for Multi-Agent Teams
复制标题
多智能体团队社会诊断算法设计研究
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
G. Kaminka
中科院分区：
文献类型：
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作者：
Meir Kalech;G. Kaminka
团队合作需要团队成员之间达成一致，才能有效地进行协作和协调。当队友之间发生分歧(由于失败)时，团队成员应该理想地诊断其原因，以解决分歧。这种对社交失败的诊断可能会在通信和计算开销方面代价高昂，这是以前的工作没有解决的问题。我们提出了一种新的诊断算法设计空间，区分了诊断过程中的几个阶段，并为每个阶段提供了可选的算法。然后，我们以不同的方式组合这些算法，以经验的方式探索复杂领域中的特定设计选择，涉及数千个失败案例。结果表明，诊断消歧过程的集中化是减少通信的关键因素，而运行时间主要受其他智能体的推理量的影响。这些结果与以前在分歧检测方面的工作形成了鲜明对比，在分歧检测中，分布式算法减少了通信。
Teamwork demands agreement among teammembers to collaborate and coordinate effectively. When a disagreement between teammates occurs (due to failures), team-members should ideally diagnose its causes, to resolve the disagreement. Such diagnosis of social failures can be expensive in communication and computation overhead, which previous work did not address. We present a novel design space of diagnosis algorithms, distinguishing several phases in the diagnosis process, and providing alternative algorithms for each phase. We then combine these algorithms in different ways to empirically explore specific design choices in a complex domain, on thousands of failure cases. The results show that centralizing the diagnosis disambiguation process is a key factor in reducing communications, while run-time is affected mainly by the amount of reasoning about other agents. These results contrast sharply with previous work in disagreement detection, in which distributed algorithms reduce communications.