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Reward Allotment Considered Roles for Learning Classifier System For Soccer Video Games

In recent years, the video-game environment has begun to change due to the explosive growth of the Internet. As a result, it makes the time for maintenance longer and the development cost increased. In addition, the life cycle of the game program shortens. To solve the above-mentioned problem, we have already proposed the event-driven hybrid learning classifier system and showed that the system is effective to improving the game winning rate and making the learning time shorten. This paper describes the investigation result of the effect in case we apply the reward allotment considered each role for classifier learning system. Concretely, we investigate the influence to each player's actions by changing the algorithm of the opponent and to team strategy by changing reward setting, and analyze them. As a result, we show that the influence of learning effects to each player's actions does not depend on the algorithm of opponent. And we also show that the reward allotment considered each role has possible to evolve the game strategy to improving the game winning rate