Meir Kalech

Teamwork demands agreement among team-members in order 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, 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 algorithms 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 diagnosis 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 diagnosis), in which distributed algorithms reduce communications.