Many different approaches to game playing have been suggested including alpha-beta search, temporal difference learning, genetic algorithms, and coevolution. Here, a powerful new algorithm for neuroevolution, Neuro-Evolution for Augmenting Topologies (NEAT), is adapted to the game playing domain. Evolution and coevolution were used to try and develop neural networks capable of defeating an alpha-beta search Othello player. While standard evolution outperformed coevolution in experiments, NEAT did develop an advanced mobility strategy. Also we demonstrated the need for protection of long-term strategies in coevolution. NEAT established its potential to enter the game playing arena and illustrated the necessity of the mobility strategy in defeating a powerful positional player in Othello.

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Technical Report HR-02-01, Department of Computer Sciences, The University of Texas at Austin.

@techreport{andersen:ugthesis02, title={Neuroevolution through Augmenting Topologies Applied to Evolving Neural Networks to Play Othello}, author={Timothy Andersen}, number={HR-02-01}, school={Department of Computer Sciences, The University of Texas at Austin}, institution={Department of Computer Sciences, The University of Texas at Austin}, type={Undergraduate Honors Thesis}, url="http://www.cs.utexas.edu/users/ai-lab?andersen:ugthesis02", year={2002} }