Event-driven hybrid learning classifier systems for online soccer games

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 videogame 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.