An Extended Behavior Network for a Game Agent: An Investigation of Action Selection Quality and Agent Performance in Unreal Tournament

This work describes an application of extended behavior networks to the control of an agent in the game Unreal Tournament. Extended Behavior Networks (EBNs) are a class of action selection architectures capable of selecting a good set of actions for complex agents situated in continuous and dynamic environments. They have been successfully applied to the Robocup, but never before used in computer games. We verify the quality of the action selection mechanism and its correctness in a series of experiments. Then we asses the performance of an agent using an EBN against a plain reactive agent with identical sensory-motor apparatus and against a totally different agent built around finite-state machines. We discuss the results of our experiments, point our future work and conclude that extended behavior networks are a good control mechanism for game agents.