dependencies on player formation in event-driven hybrid learning classifier systems for soccer video games
In this paper, we discuss dependencies on player formation when using a classifier system in a decision algorithm for agents in a soccer game. Our aim is to respond to the changing environment of video gaming that has resulted from the growth of the Internet, and to provide bug-free programs in a short time. We have already proposed a bucket brigade algorithm and a procedure for choosing what to learn depending on the frequency of events with the aim of facilitating real-time learning while a game is in progress. We have also proposed a hybrid system configuration that combines existing algorithm strategies with a classifier system, and we have reported on the effectiveness of this hybrid system. In this paper, we pit players in several different formations against each other and show that the proposed system is able to learn regardless of the differences in formation. We also show that by performing simulations ahead of time, it is possible to investigate formations that will be effective against an opponent's formation. Finally, by investigating changes in frequency and success rates for each type of play due to changes in formation, we show that it is possible to acquire a team strategy for the current formation through learning.
event-driven learning classifier systems soccer game real-time learning video-game