C4.5: programs for machine learning
C4.5: programs for machine learning
RoboCup: The Robot World Cup Initiative
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Learning models of other agents using influence diagrams
UM '99 Proceedings of the seventh international conference on User modeling
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Towards any-team coaching in adversarial domains
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automatic Symbolic Modelling of Co-evolutionarily Learned Robot Skills
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
The CMUnited-99 Champion Simulator Team
RoboCup-99: Robot Soccer World Cup III
Defining and Using Ideal Teammate and Opponent Agent Models
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Probabilistic policy reuse in a reinforcement learning agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
RESC: an approach for real-time, dynamic agent tracking
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Fast and complete symbolic plan recognition
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning models of intelligent agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Predicting opponent actions by observation
RoboCup 2004
An overview on opponent modeling in RoboCup soccer simulation 2D
Robot Soccer World Cup XV
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In competitive domains, some knowledge about the opponent can give players a clear advantage. This idea led many people to propose approaches that automatically acquire models of opponents, based only on the observation of their input-output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the behavior of the opponent. However, that is not the case in the RoboCup domain where an agent does not have direct access to the opponent inputs and outputs. Rather, the agent sees the opponent behavior from its own point of view and inputs and outputs (actions) have to be inferred from observation. In this paper, we present an approach to model low-level behavior of individual opponent agents. First, we build a classifier to infer and label opponent actions based on observation. Second, our agent observes an opponent and labels its actions using the previous classifier. From these observations, machine learning techniques generate a model that predicts the opponent actions. Finally, the agent uses the model to anticipate opponent actions. In order to test our ideas, we have created an architecture called OMBO (Opponent Modeling Based on Observation). Using OMBO, a striker agent can anticipate goalie actions. Results show that in this striker-goalie scenario, scores are significantly higher using the acquired opponent's model of actions.