GP-Gammon: Genetically Programming Backgammon Players

J. R. Anderson, and C. Lebiere, The Atomic Components of Thought, Lawrence Erlbaum Associates: Mahwah, NJ, 1998.

K. Chellapilla, “A preliminary investigation into evolving modular programs without subtree crossover,”. in, Genetic Programming 1998: Proceedings of the Third Annual Conference, J. R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D. B. Fogel, M. H. Garzon, D. E. Goldberg, H. Iba, and R. Riolo (Eds.). University of Wisconsin, Madison, Wisconsin, USA, 1998, pp. 23–31.

F. Dahl, “JellyFish Backgammon,” 1998–2004. http://www.jellyfish-backgammon.com.

P. Darwen, “Why co-evolution beats temporal-difference learning at backgammon for a linear architecture, but not a non-linear architecture,” in Proceedings of the 2001 Congress on Evolutionary Computation (CEC-01). Seoul Korea, 2001, pp. 1003–1010.

R. Gross, K. Albrecht, W. Kantschik, and W. Banzhaf, “Evolving chess playing programs,” in, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, W. B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska (Eds.). New York, 2002, pp. 740–747.

A. Hauptman, and M. Sipper, “GP-EndChess: Using genetic programming to evolve chess endgame players,” in Proceedings of 8th European Conference on Genetic Programming (EuroGP2005), M. Keijzer, A. Tettamanzi, P. Collet, J. van Hemert, and M. Tomassini, (Eds), vol. 3447 of Lecture Notes in Computer Science, Springer-Verlag, Heidelberg, 2005, pp. 120–131.

J. R. Koza, Genetic programming: On the Programming of Computers by Means of Natural Selection. MIT Press: Cambridge, MA, 1992.

J. R. Koza, Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press: Cambridge, Massachusetts, 1994.

J. R. Koza, F. H. Bennett III, D. Andre, and M. A. Keane, Genetic Programming III: Darwinian Invention and Problem Solving, Morgan Kaufmann: San Francisco, California, 1999.

D. J. Montana, “Strongly typed genetic programming,” Evolutionary Computation, vol. 3, no. 2, pp. 199–230, 1995.

J. B. Pollack, A. D. Blair, and M. Land, “Coevolution of a backgammon player,” in, Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems, C. G. Langton and K. Shimohara (Eds.), Cambridge, MA, 1997, pp. 92–98.

J. B. Pollack, A. D. Blair, and M. Land, “DEMO Lab”s HC-Gammon”, 1997. http://demo.cs.brandeis.edu/bkg.html.

D. Qi, and R. Sun, “Integrating reinforcement learning, bidding and genetic algorithms,” in Proceedings of the International Conference on Intelligent Agent Technology (IAT-2003), 2003, pp. 53–59.

E. M. A. Ronald, M. Sipper, and M. S. Capcarrère, “Design, Observation, Surprise! A Test of Emergence,” Artificial Life vol. 5, no. 3, pp. 225–239, 1999.

S. Sanner, J. R. Anderson, C. Lebiere, and M. Lovett, “Achieving efficient and cognitively plausible learning in backgammon,” in, Proceedings of the 17th International Conference on Machine Learning (ICML-2000), P. Langley (Ed.), Stanford, CA, 2000, pp. 823–830.

Y. Shichel, E. Ziserman, and M. Sipper, “GP-Robocode: Using genetic programming to evolve robocode players,” in Proceedings of 8th European Conference on Genetic Programming (Euro GP2005), M. Keijzer, A. Tettamanzi, P. Collet, J. van Hemert, and M. Tomassini, (Eds), vol. 3447 of Lecture Notes in Computer Science, Springer-Verlag, Heidelberg, 2005, pp. 143–154.

M. Sipper, “A success story or an old wives' Tale? On judging experiments in evolutionary computation,” Complexity, vol. 5, no. 4, pp. 31–33, 2000.

G. Tesauro, “NEUROGAMMON: A neural-network backgammon learning program,” Heuristic Programming in Artificial Intelligence, vol. 1, no. 7, pp. 78–80, 1989.

G. Tesauro, “Software–Source Code Benchmark player ‘pubeval.c’”. http://www.bkgm.com/rgb/rgb.cgi?view+610 1993.

G. Tesauro, “Temporal difference learning and TD-Gammon,” Communications of the ACM, vol. 38, no. 3, pp. 58–68, 1995.

X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447, 1999.