A Bayesian model of plan recognition
Artificial Intelligence
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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
Coaching a simulated soccer team by opponent model recognition
Proceedings of the fifth international conference on Autonomous agents
The CMUnited-99 Champion Simulator Team
RoboCup-99: Robot Soccer World Cup III
ChaMeleons-01 Team Description
RoboCup 2001: Robot Soccer World Cup V
Multiagent coordination by Extended Markov Tracking
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Robust recognition of physical team behaviors using spatio-temporal models
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A case-based approach for coordinated action selection in robot soccer
Artificial Intelligence
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AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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ACM Transactions on Intelligent Systems and Technology (TIST)
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An overview on opponent modeling in RoboCup soccer simulation 2D
Robot Soccer World Cup XV
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In multiagent adversarial domains, team agents should adapt to the environment and opponent. We introduce a model representation as part of a planning process for a simulated soccer domain. The planning is centralized, but the plans are executed in a multi-agent environment, with teammate and opponent agents. Further, we present a recognition algorithm where the model which most closely matches the behavior of the opponents can be selected from few observations of the opponent. Empirical results are presented to verify that important information is maintained through the abstraction the models provide.