Action Reaction Learning: Automatic Visual Analysis and Synthesis of Interactive Behaviour
ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
Cooperative Behavior Acquisition in a Multiple Mobile Robot Environment by Co-evolution
RoboCup-98: Robot Soccer World Cup II
Multiple Reward Criterion for Cooperative Behavior Acquisition in a Muliagent Environment
RoboCup-99: Robot Soccer World Cup III
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This paper proposes a method which estimates the relationships between learner's behaviors and other agents' ones in the environment through interactions (observation and action) using the method of system identification. In order to identify the model of each agent, Akaike's Information Criterion is applied to the results of Canonical Variate Analysis for the relationship between the observed data in terms of action and future observation. Next, reinforcement learning based on the estimated state vectors is performed to obtain the optimal behavior. The proposed method is applied to a soccer playing situation, where a rolling ball and other moving agents are well modeled and the learner's behaviors are successfully acquired by the method. Computer simulations and real experiments are shown and a discussion is given.