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.
|Number of pages||23|
|State||Published - Jun 2007|
Bibliographical noteFunding Information:
This paper extends and builds on an IJCAI-2003 paper by the authors, titled “On the Design of Social Diagnosis Algorithms for Multi-Agent Teams” . This research was supported in part by BSF grant #2002401. We thank Shmuel Tomi Klein and Moshe Koppel for helping with the minimal queries algorithm. We thank the anonymous reviewers for their useful suggestions on how to improve the paper. As always, thanks to K. Ushi and K. Ravit.
- Multi-agent systems
- Situated agents