Learning Human-like Movement Behavior for Computer Games
Christian Thurau, Christian Bauckage, Gerhard Sagerer · The MIT Press eBooks · 2004
Modern Computer Game AI still relies on rule-based approaches, so far failing to develop a convinc-ing, human-like opponent. Towards the development of more human-like computer game agents, we pro-pose an approach for learning strategies by observa-tion 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 learn-ing potential fields approach, establishing a mapping from world state vectors to corresponding potential field forces for agent guidance. The training data be-ing used is acquired by observation of human players acting in the 3D game environment of a commercial computer game. Finally, some experiments are pre-sented. 1.