Towards model-based diagnosis of coordination failures

With increasing deployment of multi-agent and distributed systems, there is an increasing need for failure diagnosis systems. While successfully tackling key challenges in multi-agent settings, model-based diagnosis has left open the diagnosis of coordination failures, where failures often lie in the boundaries between agents, and thus the inputs to the model - with which the diagnoser simulates the system to detect discrepancies - are not known. However, it is possible to diagnose such failures using a model of the coordination between agents. This paper formalizes model-based coordination diagnosis, using two coordination primitives (concurrence and mutual exclusion). We define the consistency-based and abductive diagnosis problems within this formalization, and show that both are NP-Hard by mapping them to other known problems. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.