Abstract
We have been exploring the potential for a co-evolutionary process to learn how to play checkers without relying on the usual inclusion of human expertise in the form of features that are believed to be important to playing well. In particular, we have focused on the use of a population of neural networks, where each network serves as an evaluation function to describe the quality of the current board position. After only a little more than 800 generations, the evolutionary process has generated a neural network that can play checkers at the expert level as designated by the U.S. Chess Federation rating system. This has been documented against real players with games played over the Internet. Our checkers program, named Anaconda, has also competed well against commercially available software.
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Chellapilla, K., Fogel, D.B. (2001). Evolving a Neural Network to Play Checkers without Human Expertise. In: Baba, N., Jain, L.C. (eds) Computational Intelligence in Games. Studies in Fuzziness and Soft Computing, vol 62. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1833-8_2
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DOI: https://doi.org/10.1007/978-3-7908-1833-8_2
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