With increasing deployment of robotic and agent teams in complex, dynamic
settings, there is an increasing need to also be able to respond to failures that
occur in teamwork . One type of failure in teamwork is disagreement,
where agents come to disagree on salient aspects of their joint task. There is
thus a particular need to be able to detect and diagnose the causes for Teamwork demands agreement among team-members in order to collaborate and
coordinate effectively. When a disagreement between teammates occurs (due to fail-
ures), team-members should ideally diagnose its causes, to resolve the disagreement.
Such diagnosis of social failures can be expensive in communication and computa-
tion, challenges which previous work has not addressed. 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 algo-
rithms in different ways to empirically explore specific design choices in a complex
domain, on thousands of failure cases. The results show that different phases of di-
agnosis affect communication and computation overhead. In particular, centralizing
the diagnosis disambiguation process is a key factor in reducing communications,
while runtime is affected mainly by the amount of reasoning about other agents.
These results contrast with previous work in disagreement detection (without diag-
nosis), in which distributed algorithms reduce communications.
disagreements that may occur, in order to facilitate recovery and reestablishment
of collaboration, e.g., by negotiations . This type of diagnosis is called social diagnosis, since it focuses on finding causes for failures to maintain social
relationships , i.e., coordination failures.
用软件翻译的不伦不类，这篇论文的专业词语太多，我翻译不了，找个高人把摘要翻译掉给我指条明路~
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