PUB - Publikationen an der Universität Bielefeld

Recent findings in biological neuroscience suggest that the brain learns body movements as sequences of motor primitives. Simultaneously, this principle is gaining popularity in robotics, computer graphics and computer vision: movement primitives were successfully applied to robotic control tasks as well as to render or to recognize human behavior. In this paper, we demonstrate that movement primitives can also be applied to the problem of implementing lifelike computer game characters. We present an approach to behavior modeling and learning that integrates several pattern recognition and machine learning techniques: trained with data from recorded multiplayer computer games, neural gas networks learn topological representation of virtual worlds; PCA is used to identify elementary movements the human players repeatedly executed during a match and complex behaviors are represented as probability functions mapping movement primitives to locations in the game environment. Experimental results underline that this framework produces game characters with humanlike skills.