PUB - Publikationen an der Universität Bielefeld

Superior weapon handling is the road to success in first person shooter games. On the expert level of gameplay, this demands more than just accurate tracking and targeting of opponents. It also requires knowledge about the behavior and characteristics of different weapons in the player’s arsenal. Given enough practice, human players usually master both skills. Usual gamebots, in contrast, can only track and target but lack the sense for the appropriateness of a weapon in a given situation. As a consequence, their performance and behavior appears non-natural. In this paper, we propose to make use of machine learning techniques to realize aiming behavior that is adapted to the spatio-temporal context. We will present a mixture of experts architecture of neural networks which is specialized in the use of several weapons. Experimental result show that this paradigm indeed is able to produce more human-like aiming behavior.