Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
Collaborative plans for complex group action
Artificial Intelligence
Needles in a haystack: plan recognition in large spatial domains involving multiple agents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Exception handling in agent systems
Proceedings of the third annual conference on Autonomous Agents
Proceedings of the third annual conference on Autonomous Agents
A framework for recognizing multi-agent action from visual evidence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Using self-diagnosis to adapt organizational structures
Proceedings of the fifth international conference on Autonomous agents
Towards robust teams with many agents
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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
An analysis of multi-agent diagnosis
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Experiences Acquired in the Design of RoboCup Teams: A Comparison of Two Fielded Teams
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An Automated Teamwork Infrastructure for Heterogeneous Software Agents and Humans
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A protocol for multi-agent diagnosis with spatially distributed knowledge
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An Experimental Evaluation of Domain-Independent Fault Handling Services in Open Multi-Agent Systems
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
The Adaptive Agent Architecture: Achieving Fault-Tolerance Using Persistent Broker Teams
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Diagnosis as an Integral Part of Multi-Agent Adaptability TITLE2:
Diagnosis as an Integral Part of Multi-Agent Adaptability TITLE2:
Diagnosing a team of agents: scaling-up
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An integrated token-based algorithm for scalable coordination
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Towards flexible teamwork in behavior-based robots: extended abstract
Proceedings of the fourth 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
An architecture for exception management in multiagent systems
International Journal of Agent-Oriented Software Engineering
Towards model-based diagnosis of coordination failures
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Robust agent teams via socially-attentive monitoring
Journal of Artificial Intelligence Research
Interactive execution monitoring of agent teams
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Monitoring teams by overhearing: a multi-agent plan-recognition approach
Journal of Artificial Intelligence Research
Tracking dynamic team activity
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
An application perspective evaluation of multi-agent system in versatile environments
Expert Systems with Applications: An International Journal
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Intermittent sensory, actuation and communication failures may cause agents to fail in maintaining their commitments to others. Thus to collaborate robustly, agents must monitor others to detect coordination failures. Previous work on monitoring has focused mainly on small-scale systems, with only a limited number of agents. However, as the number of monitored agents is scaled up, two issues are raised that challenge previous work. First, agents become physically and logically disconnected from their peers, and thus their ability to monitor each other is reduced. Second, the number of possible coordination failures grows exponentially, with all potential interactions. Thus previous techniques that sift through all possible failure hypotheses cannot be used in large-scale teams. This paper tackles these challenges in the context of detecting disagreements among team-members, a monitoring task that is of particular importance to robust teamwork. First, we present new bounds on the number of agents that must be monitored in a team to guarantee disagreement detection. These bounds significantly reduce the connectivity requirements of the monitoring task in the distributed case. Second, we present YOYO, a highly scalable disagreement-detection algorithm which guarantees sound detection. YOYO's run-time scales linearly in the number of monitored agents, despite the exponential number of hypotheses. It compactly represents all valid hypotheses in single structure, while allowing for a complex hierarchical organizational structure to be considered in the monitoring. Both YOYO and the new bounds are explored analytically and empirically in monitoring problems involving thousands of agents.