Evolutionary swarm neural network game engine for Capture Go

Neural Networks

Volumn 23, Issue 2, 2010, Pages 295-305

  Cai, Xindi ^a   Venayagamoorthy, Ganesh K ^b   Wunsch II, Donald C ^b  

^a APC MGE by Schneider Electric   (United States)

^b Architectural and Environmental Engineering   (United States)

Author keywords

Capture Go; Evolutionary algorithm; Evolutionary computation; Game engine; Go; Neural networks; Particle swarm optimization

Indexed keywords

BOARD POSITION; BRUTE FORCE SEARCH; COMBINATION OF NEURAL-NETWORK; COMPUTER GAME ENGINE; EVOLUTIONARY COMPUTATION; EVOLUTIONARY COMPUTATIONS; EXPERT KNOWLEDGE; GAME ENGINE; GO; HIGH-DIMENSIONAL; HILL CLIMBING; HYBRID ALGORITHMS; SELF-PLAY; ZERO KNOWLEDGE;

ALGORITHMS; COMPUTATION THEORY; ENGINES; LOCOMOTIVES; NEURAL NETWORKS; RAILROAD CARS;

PARTICLE SWARM OPTIMIZATION (PSO);

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; EVOLUTIONARY ALGORITHM; GAME; KNOWLEDGE BASE; PRIORITY JOURNAL; RECREATION; SIMULATION;

ALGORITHMS; COMPUTER SIMULATION; EVOLUTION; GAMES, EXPERIMENTAL; LEARNING; NEURAL NETWORKS (COMPUTER);

EID: 73949115540     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2009.11.001     Document Type: Article

References (57)

1. Allis L.V., van den Herik H.J., and Herschberg I.S. Which games will survive?. In: Levy D.N.L., and Beal D.F. (Eds). Heuristic programming in artificial intelligence 2-The second computer olympiad (1991), Ellis Horwood 232-243
2. Axelrod R. The evolution of cooperation (1984), Basic Books, New York
3. Berlekamp E., and Wolfe D. Mathematical Go: Chilling gets the last point (1994), A.K. Peter Ltd., MA
4. Cai, X., & Wunsch, D. II (2004). Evolutionary computation in playing Capture Go game. In Proc. of international conference on cognitive and neural systems
5. Cai X., Zhang N., Venayagamoorthy G.K., and Wunsch II D. Time series prediction with recurrent neural networks using hybrid PSO-EA algorithm. Neurocomputing 70 13-15 (2006) 2342-2353
6. Chellapilla K., and Fogel D. Evolution, neural networks, games, and intelligence. Proceedings of the IEEE 87 9 (1999) 1471-1496
7. Chellapilla K., and Fogel D. Evolving neural networks to play Checkers without relying on expert knowledge. IEEE Transactions on Neural Networks 10 6 (1999) 1382-1391
8. Chellapilla K., and Fogel D. Evolving an expert Checkers playing programs without using human expertise. IEEE Transactions on Evolutionary Computation 5 4 (2001) 422-428
9. Chen X., Zhang D., Zhang X., Li Z., Meng X., He S., et al. A functional MRI study of high-level cognition II. The game of Go. Cognitive Brain Research 16 1 (2003) 32-37
10. Clerc M., and Kennedy J. The particle swarm: Explosion, stability, and convergence in a multi-dimension complex space. IEEE Transactions on Evolutionary Computation 6 1 (2002) 58-73
11. Doctor, S., Venayagamoorthy, G. K., & Gudise, V. G. (2004). Optimal PSO for collective robotic search applications. In Proc. of the IEEE congress on evolutionary computation (pp. 1390-1395)
12. Doctor, S., & Venayagamoorthy, G. K. (2005). Improving the performance of particle swarm optimization using adaptive critics designs. IEEE swarm intelligence symposium
13. Enderton, H. D. (1991). The golem Go program. Tech. rep. CMU-CS-92-101. Carnegie Mellon University
14. Enzenberger, M. (1996). The integration of a priori knowledge into a Go playing neural network. Available: http://www.markus-enzenberger.de/neurogo.ps.gz
15. Engelbrecht A.P. Computational intelligence-An introduction (2002), Wiley. 0-470-84870-7
16. Esquivel S.C., and Coello Coello C.A. Hybrid particle swarm optimizer for a class of dynamic fitness landscape. Engineering Optimization 38 8 (2006) 873-888
17. Fogel D. Blondie24: Playing at the edge of AI (2002), Morgan Kaufmann, SF, CA
18. Fogel D., Hays T.J., Hahn S.L., and Quon J. A self-learning evolutionary Chess program. Proceedings of the IEEE 92 12 (2004) 1947-1954
19. Fogel, D., Hays, T. J., Hahn, S. L., & Quon, J. (2005). Further evolution of a self-learning Chess program. Proc. of IEEE symposium on computational intelligence in games (pp. 73-77)
20. Fotland, D. (1999). The 1999 FOST (Fusion of Science & Technology) cup world open computer championship, Tokyo. Available: http://www.britgo.org/results/computer/fost99htm
21. Fürnkranz J. Machines that learn to play games. In: Fürnkranz J., and Kubat M. (Eds). Machine learning in games: A survey (2001), Nova Scientific Publishers, Huntington, NY 11-59 [Chapter 2]
22. Gomez F., and Miikkulainen R. Incremental evolution of complex general behavior. Adaptive Behavior 5 (1997) 317-342
23. Gudise, V. G., & Venayagamoorthy, G. K. (2003). Comparison of particle swarm optimization & backpropagation as training algorithms for neural networks. IEEE swarm intelligence symposium (pp. 110-117)
24. Higashi, N., & Iba, H. (2003). Particle swarm optimization with Gaussian mutation. In Proc. of IEEE swarm intelligence symposium (pp. 72-79)
25. Hsu F. Behind deep blue (2002), Princeton Univ. Press, Princeton, NJ
26. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In IEEE international conference on neural networks (pp. 1942-1948). Vol. 4
27. Kennedy J., Eberhart R., and Shi Y. Swarm intelligence (2001), Morgan Kaufmann, San Meteo, CA
28. Konidaris, G., Shell, D., & Oren, N. (2002). Evolving neural networks for the capture game. In Proc. of the 2002 annual research conference of the south African institute of computer scientists & information technologists postgraduate symposium. Available from: http://www-robotics.usc.edu/~dshell/res/evneurocapt.pdf
29. Maynard Smith J. Evolution & the theory of games (1982), Cambridge University Press, Cambridge, UK
30. Mechner D. All systems Go. The Sciences 38 1 (1998)
31. Messerschmidt L., and Engelbrecht A.P. Learning to play games using a PSO-based competitive learning approach. IEEE Transactions on Evolutionary Computation 8 3 (2004) 280-288
32. Miranda, V., & Fonseca, N. (2002). EPSO-best-of-two-worlds meta-heuristic applied to power system problems. In Proc. of IEEE congress on evolutionary computation (pp. 1080-1085)
33. Morgenstern O. Economics & the theory of games. Kyklos 3 4 (1949) 294-308
34. Morris P. Introduction to game theory (1994), Springer-Verlag, NY
35. Muller, M. (1999). Decomposition search: A combinatorial games approach to game tree search, with applications to solving Go endgames. In Proc. of international joint conference on artificial intelligence (pp. 578-583)
36. Pollack J.B., and Blair A.D. Co-evolution in the successful learning of Backgammon strategy. Machine Learning 32 (1998) 226-240
37. Richards N., Moriarty D., Mc Questen P., and Miikkulainen R. Evolving neural networks to play Go. Applied Intelligence 8 (1998) 85-96
38. Runarsson T.P., and M Lucas S. Co-evolution versus self-play temporal difference learning for acquiring position evaluation in small-board Go. IEEE Transactions on Evolutionary Computation 9 6 (2005) 628-640
39. http://www.schachverein-goerlitz.de/Foren/Fun/Go/go.htm
40. Schaeffer J. One jump ahead: Challenging human supremacy in Checkers (1996), Springer, New York
41. Schraudolph N., Dayan P., and Sejnowski T. Temporal difference learning of position evaluation in the game of Go. Advances in Neural Information Processing 6 (1994) 817-824
42. Schwefel H.P. Evolution & optimum seeking (1995), Wiley, NY
43. Shannon C.E. Automatic chess player. Scientific American 182 2 (1950) 48-51
44. Shi, Y., & Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. In Proceedings of the 1998 annual conference on evolutionary computation
45. Shi, Y., & Eberhart, R. C. (2001). Fuzzy adaptive particle swarm optimization. In Proceedings of the 2001 Congress on evolutionary computation
46. Shi, Y., & Eberhart, R. C. (2001). Particle swarm optimization with fuzzy adaptive inertia weight. In Proceedings of the workshop on particle swarm optimization
47. Shi Y. Particle swarm optimization. IEEE connectors, the Newsletter of the IEEE Neural Networks Society Vol. 2(1) (2004) 8-13
48. Smith A. The game of Go (1956), Charles Tuttle Co., Tokyo, Japan
49. Ting, T. O., Wong, K. P., & Chung, C. Y. (2005). Investigation of hybrid genetic algorithm/particle swarm optimization approach for the power flow problem. In Proc. of international conference on machine learning & cybernetics 2005 (pp. 436-440)
50. Trelea L.C. The particle swarm optimization algorithm: Convergence analysis & parameter selection. Information Processing Letters 85 6 (2003) 317-325
51. van den Bergh, F. (2002). An analysis of particle swarm optimizers. Ph.D. dissertation. Dept. of Computer Science. Univ. Pretoria, Pretoria, South Africa
52. van den Bergh F., and Engelbrecht A.P. A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8 3 (2004) 225-239
53. von Neumann J., and Morgenstern O. Theory of games & economic behavior (1944), Princeton Univ. Press, Princeton, NJ
54. Wolpert D.H., and Macready W.G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 1 (2001) 67-82
55. Xia, W., Wu, Z., Zhang, W., & Yang, G. (2004). A new hybrid optimization algorithm for the job-shop scheduling problem. In Proc. of American control conference 2004 (pp. 5552-5557)
56. Zaman, R., Prokhorov, D. V., & Wunsch, D. C. II (1997). Adaptive critic design in learning to play the game of Go. In Proc. of the international joint conference on neural networks (pp. 1-4)
57. Zheng, Y., Ma, L., Zhang, L., & Qian, J. (2003). Empirical study of particle swarm optimizer with an increasing inertia weight. In Proc. of IEEE congress on evolutionary computation (pp. 221-226)