Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
Collaborative plans for complex group action
Artificial Intelligence
Exception handling in agent systems
Proceedings of the third annual conference on Autonomous Agents
Towards robust teams with many agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Two Fielded Teams and Two Experts: A RoboCup Challenge Response from the Trenches
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Diagnosis as an Integral Part of Multi-Agent Adaptability TITLE2:
Diagnosis as an Integral Part of Multi-Agent Adaptability TITLE2:
Diagnosis of multi-robot coordination failures using distributed CSP algorithms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Robust agent teams via socially-attentive monitoring
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
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Teamwork requires that team members coordinate their actions. The representation of the coordination is a key requirement since it influences the complexity and flexibility of reasoning team---members. One aspect of this requirement is detecting coordination faults as a result of intermittent failures of sensors, communication failures, etc. Detection of such failures, based on observations of the behavior of agents, is of prime importance. Though different solutions have been presented thus far, none has presented a comprehensive and formal resolution to this problem. This paper presents a formal approach to representing multiagent coordination, and multiagent observations, using matrix structures. This representation facilitates easy representation of coordination requirements, modularity, flexibility and reuse of existing systems. Based on this representation we present a novel solution for fault-detection that is both generic and efficient for large-scale teams.