The Incremental Evolution of Attack Agents in Xpilot

In the research presented in this paper, we use incremental evolution to learn multifaceted neural network (NN) controllers for agents operating in the space game Xpilot. Behavioral components specific to the accomplishment of specific tasks, such as bullet-dodging, shooting, and closing on an enemy, are learned in the first increment. These behavioral components are used in the second increment to evolve a NN that prioritizes the output of a two-layer NN depending on that agent´s current situation.