Knowledge and common knowledge in a distributed environment
Journal of the ACM (JACM)
Unified theories of cognition
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
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 plan recognition in human-computer collaboration
UM '99 Proceedings of the seventh international conference on User modeling
Monitoring deployed agent teams
Proceedings of the fifth international conference on Autonomous agents
Hierarchical agent control: a framework for defining agent behavior
Proceedings of the fifth international conference on Autonomous agents
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
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
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Adaptive Agent Integration Architectures for Heterogeneous Team Members
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Behavioral diversity in learning robot teams
Behavioral diversity in learning robot teams
Robust agent teams via socially-attentive monitoring
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Tracking dynamic team activity
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Diagnosing a team of agents: scaling-up
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
Matrix-based representation for coordination fault detection: a formal approach
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Detecting disagreements in large-scale multi-agent teams
Autonomous Agents and Multi-Agent Systems
Hypothesis pruning and ranking for large plan recognition problems
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A representation for coordination fault detection in large-scale multi-agent systems
Annals of Mathematics and Artificial Intelligence
Activity Recognition for Dynamic Multi-Agent Teams
ACM Transactions on Intelligent Systems and Technology (TIST)
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Agents in deployed multi-agent systems monitor other agents to coordinate and collaborate. However, as the number of agents monitored is scaled up, two key challenges arise: (i) the number of monitoring hypotheses to be considered can grow exponentially in the number of agents; and (ii) agents become physically and logically unconnected (unobservable) to their peers. This paper examines these challenges in teams of cooperating agents, focusing on a monitoring task that is of particular importance to robust teamwork: detecting disagreements among team-members. We present YOYO, a highly scalable disagreement-detection algorithm which guarantees sound detection in time linear in the number of agents despite the exponential number of hypotheses. In addition,we present new upper bounds for the number of agents that must be monitored in a team to guarantee disagreement detection. Both YOYO and the new bounds are explored analytically and empirically in thousands of monitoring problems, scaled to thousands of agents.