Neuroevolution has been successfully used in developing controllers for phys- ical simulation domains. However, the ability to strategize in such domains has not been studied from an evolutionary perspective. This thesis makes the following three contributions. First, it implements Neuroevolution using NEAT with a goal of evolving strategic controllers for the challenging physical simulation domain of car-racing. Second, three different evolu- tionary approaches are studied and analyzed on their ability to evolve advanced skills and strategy. Though these approaches are found to be good at evolving controllers with advanced skills, discovering high-level strategy proves to be hard. Third, a modular approach is proposed to evolve high-level strategy using Neuroevolution. Given such a suitable task decomposition, Neuroevolution succeeds in evolving con- trollers capable of strategy by using a modular approach. The simplerace car-racing simulation[29] is used as a testbed for this study. The results obtained in the car- racing domain suggest that the modular approach can be applied to evolve strategic behavior in other physical simulation domains and tasks.

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Masters Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX/US, 2008. 85 pages.

@MastersThesis{gowrisankar:ms08, title={Evolving Controllers for Simulated Car Racing using Neuroevolution}, author={Aravind Gowrisankar}, school={Department of Computer Sciences, The University of Texas at Austin}, address={Austin, TX/US}, institution={Department of Computer Sciences, The University of Texas at Austin.}, pages={85 pages}, url="http://nn.cs.utexas.edu/?gowrisankar:ms09", year={2008} }