Document Type
Conference Proceeding
Publication Date
6-2011
Abstract
In this paper we present an application of Fitness Biasing, a type of Punctuated Anytime Learning, for learning autonomous agents in the space combat game Xpilot. Fitness Biasing was originally developed as a means of linking the model to the actual robot in evolutionary robotics. We use fitness biasing with a standard genetic algorithm to learn control programs for a video game agent in real-time. Xpilot-AI, an Xpilot add-on designed for testing learning systems, is used to evolve the controller 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 bots, display complex behavior that resemble human strategies, and are capable of adapting to a changing enemy in real-time.
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Recommended Citation
Parker, G.; Fritzsche, P., "Fitness Biasing for evolving an Xpilot combat agent," Evolutionary Computation (CEC), 2011 IEEE Congress on , vol., no., pp.1071,1076, 5-8 June 2011 doi: 10.1109/CEC.2011.5949736
Included in
The views expressed in this paper are solely those of the author.
Comments
©2011 IEEE
DOI:10.1109/CEC.2011.5949736