Learning to play like a human: case injected genetic algorithms for strategic computer gaming

We use case injected genetic algorithms to learn how to competently play computer strategy games. Strategic computer games involve long range planning across complex dynamics and imperfect knowledge presented to players requires them to anticipate opponent moves and adapt their strategies accordingly. This work addresses the problem of acquiring and using knowledge from human players for such games. Specifically, we learn general routing information from a human player and use case-injected genetic algorithms to incorporate this acquired knowledge in subsequent planning. Results from a strike planning game show that with an appropriate representation, case injection effectively biases the genetic algorithm toward producing plans that contain important strategic elements used by human players.