Archetypical motion: Supervised game behavior learning with Archetypal Analysis
The problem of creating believable game AI poses numerous challenges for computational intelligence research. A particular challenge consists in creating human-like behaving game bots by means of applying machine learning to game-play data recorded by human players. In this paper, we propose a novel, biologically inspired approach to behavior learning for video games. Our model is based on the idea of movement primitives and we use Archetypal Analysis to determine elementary movements from data in order to represent any player action in terms of convex combinations of archetypal motions. Given these representations, we use supervised learning in order to create a system that is able to synthesize appropriate motion behavior during a game. We apply our model to teach a first person shooter game bot how to navigate in a game environment. Our results indicate that the model is able to simulate human-like behavior at lower computational costs than previous approaches.