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 that involve long range planning across complex dynamics. Imperfect knowledge presented to players requires them adapt their strategies in order to anticipate opponent moves. We focus on the problem of acquiring knowledge learned from human players, in particular we learn general routing information from a human player in the context of a strike force planning game. By incorporating case injection into a genetic algorithm, we show methods for incorporating general knowledge elicited from human players into future plans. In effect allowing the GA to take important strategic elements from human play and merging those elements into its own strategic thinking. Results show that with an appropriate representation, case injection is effective at biasing the genetic algorithm toward producing plans that contain important strategic elements used by human players