Agents in a team should be in agreement. Unfortunately, they may come to disagree due to sensor uncertainty, intermittent communication failures, etc. Once a disagreement occurs, the agents should detect and diagnose the disagreement. Current diagnostic techniques do not scale well with the number of agents, as they have high communication and computation complexity. We present novel techniques that enable scalability in three ways. First, we use communications early in the diagnostic process to stave off unneeded reasoning, which ultimately leads to unneeded communications. Second, we use light-weight (and inaccurate) behavior recognition to focus the diagnostic reasoning on beliefs of agents that might be in conflict. Finally, we propose diagnosing only to a limited number of representative agents (instead of all the agents). We examine these techniques in large-scale teams of situated agents in two domains and show that combining the techniques produces a diagnostic process that is highly scalable in both communication and computation.
['Meir Kalech', 'Gal A. Kaminka']
COORDINATION DIAGNOSTIC ALGORITHMS FOR TEAMS OF SITUATED AGENTS: SCALING UP