Evolutionary Behavior Tree Approaches for Navigating Platform Games

Computer games are highly dynamic environments, where players are faced with a multitude of potentially unseen scenarios. In this paper, AI controllers are applied to the Mario AI benchmark platform, by using the grammatical evolution system to evolve behavior tree structures. These controllers are either evolved to both deal with navigation and reactiveness to elements of the game or used in conjunction with a dynamic A* approach. The results obtained highlight the applicability of behavior trees as representations for evolutionary computation and their flexibility for incorporation of diverse algorithms to deal with specific aspects of bot control in game environments.