C4.5: programs for machine learning
C4.5: programs for machine learning
Towards any-team coaching in adversarial domains
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
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-98 Champion Simulator Team
RoboCup-98: Robot Soccer World Cup II
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
The RoboCup synthetic agent challenge 97
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
OMBO: An opponent modeling approach
AI Communications
A comparing method of two team behaviours in the simulation coach competition
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
An overview on opponent modeling in RoboCup soccer simulation 2D
Robot Soccer World Cup XV
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In competitive domains, the knowledge about the opponent can give players a clear advantage. This idea lead us in the past to propose an approach to 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 opponent. However, that is not the case in the Robocup domain. To overcome this problem, in this paper we present a three phases approach to model low-level behavior of individual opponent agents. First, we build a classifier to label opponent actions based on observation. Second, our agent observes an opponent and labels its actions using the previous classifier. From these observations, a model is constructed to predict the opponent actions. Finally, the agent uses the model to anticipate opponent reactions. In this paper, we have presented a proof-of-principle of our approach, termed OMBO (Opponent Modeling Based on Observation), so that a striker agent can anticipate a goalie. Results show that scores are significantly higher using the acquired opponent's model of actions.