Learning Human-Like Movement Behavior for Computer Games

Thurau C, Bauckhage C, Sagerer G. Learning Human-Like Movement Behavior for Computer Games. In: Proc. 8th Int. Conf. on the Simulation of Adaptive Behavior (SAB’04). 2004.Modern Computer Game AI still relies on rulebased approaches, so far failing to develop a convincing, human-like opponent. Towards the development of more human-like computer game agents, we propose an approach for learning strategies by observation of human players, principally viewing the design of a computer game agent as a problem of pattern recognition. First we introduce a Neural Gas based grid learning of an internal representation of the 3D game-world. Then we discuss the use of a learning potential fields approach, establishing a mapping from world state vectors to corresponding potential field forces for agent guidance. The training data being used is acquired by observation of human players acting in the 3D game environment of a commercial computer game. Finally, some experiments are presented