This paper reports on the application of classifier systems to the acquisition of decision-making algorithms for agents in online soccer games. The objective of this research is to support changes in the video-game environment brought on by the Internet and to enable the provision of bug-free programs in a short period of time. To achieve real-time learning during a game, a bucket brigade algorithm is used to reinforce learning by classifiers and a technique for selecting learning targets according to event frequency is adopted. A hybrid system combining an existing strategy algorithm and a classifier system is also employed. In experiments that observed the outcome of 10,000 soccer games between this event-driven classifier system and a human-designed algorithm, the proposed system was found to be capable of learning effective decision-making algorithms in real time.
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