Synthesizing Movements for Computer Game Characters Christian Thurau, Christian Bauckhage, and Gerhard Sagerer Faculty of Technology, Applied Computer Science Bielefeld University, P.O. Box 100131, 33501 Bielefeld, Germany cthurau,cbauckha,sagerer @techfak.uni-bielefeld.de Abstract. 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 vi- sion: 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 model- ing 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. 1 Motivation and Overview Computer games have become an enormous business; just recently, its annual sales figures even surpassed those of the global film industry [3]. While it seems fair to say that this success boosted developments in fields like computer graphics and networking, commercial game programming and modern artificial intelligence or pattern recognition hardly influenced each other. However, this situation is about to change. On the one hand, the game industry is beginning to fathom the potential of pattern recognition and machine learning to produce life-like artificial characters. On the other hand, the AI and pattern recognition communities and even roboticists discover computer games as a testbed in behavior learning and action recognition (cf. e.g. [1, 2, 10, 13]). This paper belongs to the latter category. Following an idea discussed in [2], we report on analyzing the network traffic of multiplayer games in order to realize game agents that show human-like movement skills. From a computer game perspective, this is an interesting problem because many games require the player to navigate through virtual worlds (also called maps). Practical experience shows that skilled human players do this more efficiently than their computer controlled counterparts. They make use of shortcuts or perform movements which artificial agents cannot perform simply because their programmers did not think of it 1 . 1 An example is the rocket jump in ID Software’s (in)famous game Quake II. Shortly after the game was released, players discovered that they can jump higher if they make use of the recoil