Diagnosing Coordination Faults in Multi-Agent Systems

With increasing deployment of multiple agents in complex, dynamic settings, there is an increasing need to respond to failures that occur in the agents coordination. In particular, there is need to detect and diagnose coordination failures—failures to maintain relationships between agents. We refer to this type of diagnosis as social diagnosis. Previous approaches to diagnosis in multi-agent settings have either ignored failures in coordination, or utilized heuristic approaches which do not scale up as the number of agents (and their interactions) increases. This dissertation offers a comprehensive and principled approach to social diagnosis. We use a model-based diagnosis (MBD) approach. Here, a model of a diagnosed system is used to simulate the behavior of the system given the operational context (typically, the system’s inputs), and to pinpoint possible failing components within the system. MBD has been difficult to apply to diagnosing coordination failures, because of the challenges in constructing a model of coordination, and the lack of appropriate (scalable) diagnosis algorithms. In the first part of this dissertation we formally show how to construct a model of agent coordination, and use it to formally define the two key variant social diagnosis problems: the Consistency-based diagnosis problem, and the abductive diagnosis problem. We show that these are NP-Hard problems. We then build on known methods in constraint-satisfaction problems, to provide several algorithms for social diagnosis in centralized and distributed settings. The algorithms—whose analytical guarantees vary in terms of completeness and correctness—are evaluated empirically, in experiments carried out with teams of physical robots. We examined the computational requirements of the algorithms (i.e., their run-time and bandwidth usage), and the correctness of the diagnoses produced. We find that in general, a trade-off exists between computational costs and the correctness of the diagnosis. In the second part of the dissertation, we focus on a particular type of coordination failures—disagreements—which are of particular interest when talking about teams. We examine the design space of disagreement diagnosis algorithms for a more complex class of situated agents (compared to the first part). We distinguish two phases of diagnosis: (i) the selection of the diagnosing agents; and (ii) the diagnosis of the global team state (by the selected agents). We provide alternative algorithms for these phases, and empirically evaluate their communications and run-time requirements. The results show that centralizing the disambiguation process is a key factor in improving communications, but is not a determining factor in run-time.