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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References
Schaeffer, J. (1996), One Jump Ahead: Challenging Human Supremacy in Checkers, Springer, Berlin.
Samuel, A. L. (1959), “Some studies in machine learning using the game of checkers,” IBM J. Res. Deli., vol. 3, no. 3, pp. 210–219.
Chellapilla, K. and Fogel, D. B. (1999), “Evolution, neural networks, games, and intelligence,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1471–1498.
Chellapilla. K. and Fogel, D. B. (2000) “Evolving an expert checkers playing program without using human expertise,” IEEE Trans. Pattern Analysis and Machine Intelligence, in review.
Minsky, M. L. (1961) “Steps toward artificial intelligence,” Proceedings of the IRE, vol. 49, no. 1, pp. 8–30.
Hornik, K., Stinchcombe, M., and White, H. (1989) “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, pp. 359–366.
Poggio, T. and Girosi, F. (1990) “Networks for approximation and learning,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1481–1497.
Bäck, T., Hammel, U., and Schwefel, H.-P. (1997) “Evolutionary computation: comments on the history and current state,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 3–17.
Bäck, T. (1996) Evolutionary Algorithms in Theory and Practice, Oxford, NY.
Michalewicz, Z. and Fogel, D. B. (2000) How to Solve It: Modern Heuristics, Springer, Berlin.
Fogel, L. J. (1999) Intelligence through Simulated Evolution, John Wiley, NY.
Yao, X. (1999) “Evolving artificial neural networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447.
Yao, X. and Fogel, D. B. (2000), Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks,IEEE Press, Piscataway, NJ, in press.
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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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