On the design of social diagnosis algorithms for multi-agent teams

Meir Kalech, Gal A. Kaminka

Research output: Contribution to journalConference articlepeer-review

38 Scopus citations


Teamwork demands agreement among team-members 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.

Original languageEnglish
Pages (from-to)370-375
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2003
Event18th International Joint Conference on Artificial Intelligence, IJCAI 2003 - Acapulco, Mexico
Duration: 9 Aug 200315 Aug 2003


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