Debugging multi-agent systems using design artifacts: the case of interaction protocols
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Channeled multicast for group communications
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Monitoring teams by overhearing: a multi-agent plan-recognition approach
Journal of Artificial Intelligence Research
Monitoring large-scale multi-agent systems using overhearing
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Multi-party communication and information-need anticipation by experience
Web Intelligence and Agent Systems
Role-based teamwork activity recognition in observations of embodied agent actions
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Experiments in Selective Overhearing of Hierarchical Organizations
Agent Communication II
Multiparty proactive communication: a perspective for evolving shared mental models
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Representing conversations for scalable overhearing
Journal of Artificial Intelligence Research
E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
T-compound interaction and overhearing agents
ESAW'04 Proceedings of the 5th international conference on Engineering Societies in the Agents World
Introducing participative personal assistant teams in negotiation support systems
PRIMA'04 Proceedings of the 7th Pacific Rim international conference on Intelligent Agents and Multi-Agent Systems
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Overhearing is gaining attention as a generic method for cooperative monitoring of distributed, open, multiagent systems. It involves monitoring the routine conversations of agents 驴 who know they are being overheard 驴 to assist the agents, assess their progress, or suggest advice. While there have been several investigations of applications and methods of overhearing, no formal model of overhearing exists. This paper takes steps towards such a model. It first formalizes a conversation system 驴 the set of conversations in a multi-agent system. It then defines a key step in overhearing 驴 conversation recognition 驴 identifying the conversations that took place within a system, given a set of overheard messages. We provide a skeleton algorithm for conversation recognition, and provide instantiations of it for settings involving no message loss, random message loss, and systematic message loss (such as always losing one side of the conversation). We analyze the complexity of these algorithms, and show that the systematic message loss algorithm, which is unique to overhearing, is significantly more efficient then the random loss algorithm (which is intractable).