Evolving autonomous agent control in the Xpilot environment

Interactive combat games are useful as test-beds for learning systems employing evolutionary computation. Of particular value are games that can be modified to accommodate differing levels of complexity. In this paper, the authors presented the use of Xpilot as a learning environment that can be used to evolve primitive reactive behaviors, yet can be complex enough to require combat strategies and team cooperation. In addition, this environment was used with a genetic algorithm to learn the weights for an artificial neural network controller that provides both offensive and defensive reactive control for an autonomous agent.