Distributed Algorithms
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
A protocol for multi-agent diagnosis with spatially distributed knowledge
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Towards model-based diagnosis of coordination failures
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
On the design of coordination diagnosis algorithms for teams of situated agents
Artificial Intelligence
Matrix-based representation for coordination fault detection: a formal approach
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Matrix-based representation for coordination fault detection: a formal approach
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Toward Cooperative Team-diagnosis in Multi-robot Systems
International Journal of Robotics Research
A representation for coordination fault detection in large-scale multi-agent systems
Annals of Mathematics and Artificial Intelligence
Diagnosis of coordination failures: a matrix-based approach
Autonomous Agents and Multi-Agent Systems
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With increasing deployment of systems involving multiple coordinating agents, there is a growing need for diagnosing coordination failures in such systems. Previous work presented centralized methods for coordination failure diagnosis; however, these are not always applicable, due to the significant computational and communication requirements, and the brittleness of a single point of failure. In this paper we propose a distributed approach to model-based coordination failure diagnosis. We model the coordination between the agents as a constraint graph, and adapt several algorithms from the distributed CSP area, to use as the basis for the diagnosis algorithms. We evaluate the algorithms in extensive experiments with simulated and real Sony Aibo robots and show that in general a trade-off exists between the computational requirements of the algorithms, and their diagnosis results. Surprisingly, in contrast to results in distributed CSPs, the asynchronous backtracking algorithm outperforms stochastic local search in terms of both quality and runtime.