In this paper, research is presented on an application of Punctuated Anytime Learning with Fitness Biasing, a type of computational intelligence and evolutionary learning, for real-time learning of autonomous agents controllers in the space combat game Xpilot. Punctuated Anytime Learning was originally developed as a means of effective learning in the field of evolutionary robotics. An analysis was performed on the game environment to determine optimal environmental settings for use during learning, and Fitness Biasing is employed using this information to learn intelligent behavior for a video game agent controller in real-time. Xpilot-AI, an Xpilot add-on designed for testing learning systems, is used alongside evolutionary learning techniques to evolve optimal behavior in the background while periodic checks in normal game play are used to compensate for errors produced by running the system at a high frame rate. The resultant learned controllers are comparable to our best hand-coded Xpilot-AI agents, display complex behavior that resemble human strategies, and are capable of adapting to a changing enemy in real-time. The work presented in this paper is also general enough to further the development of Punctuated Anytime Learning in evolutionary robotic systems.
Fritzsche, Phillip, "Punctuated Anytime Learning and the Xpilot-AI Combat Environment" (2011). Computer Science Honors Papers. Paper 1.
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