Neuroevolution through Augmenting Topologies Applied to Evolving Neural Networks to Play Othello (2002)
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.
Technical Report HR-02-01, Department of Computer Sciences, The University of Texas at Austin.

Timothy D. Andersen Undergraduate Alumni andert [at] alum rpi edu
NEAT C++ The NEAT package contains source code implementing the NeuroEvolution of Augmenting Topologies method. The source code i... 2010