Nannon™: A nano backgammon for machine learning research

IEEE 2005 Symposium on Computational Intelligence and Games, CIG'05

Volumn , Issue , 2005, Pages 277-284

  Pollack, Jordan B ^a  

^a BRANDEIS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CO-EVOLUTIONARY LEARNING; INDUCTIVE INFERENCE; MACHINE LEARNING RESEARCH; RULE SET; VALUE FUNCTION APPROXIMATION;

GENETIC ALGORITHMS; NEURAL NETWORKS; REINFORCEMENT LEARNING;

GENETIC PROGRAMMING;

EID: 33746637924     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Angeline, P. J. & Pollack, J. B. (1993) Competitive environments evolve better solutions to complex problems. Fifth International Conference on Genetic Algorithms. 264- 270.
2. Robert M. Axelrod (1987) The evolution of strategies in the iterated prisoner's dilemma. In Genetic Algorithms and Simulated Annealing, chapter 3, pages 32-41. Morgan Kaufmann.
3. Hans J. Berliner (1977) Experiences in Evaluation with BKG - A Program that Plays Backgammon. IJCAI 428-433.
4. De Jong, E.D. and J.B. Pollack (2004) Ideal Evaluation in Coevolution, Evolutionary Computation, Vol. 12, Issue 2, pp. 159-192.
5. Dennett, D.C. (1991) Consciousness Explained. Boston: Little, Brown.
6. Fogel, D. B (2002) BLONDIE24: Playing at the edge of AI. San Francisco: Morgan Kaufmann.
7. Hillis, D. (1992). Co-evolving parasites improve simulated evolution as an optimization procedure. In Alife II: Proceedings of the 2nd International Conference on Artificial Life. Addison-Wesley.
8. Paul Magriel. (1976) BACKGAMMON, New York: Times Books.
9. Oswald Jacoby & John R. Crawford. (1970) The Backgammon Book. New York: Viking.
10. Pollack. J B. & Blair A. (1998). Co-Evolution in the Successful Learning of Backgammon Strategy. Machine Learning, 32, 225-240. (Pubitemid 40626406)
11. Ray, T. S. (1991), An approach to the synthesis of life. In : Langton, C., C. Taylor, J. D. Farmer, & S. Rasmussen [eds], Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, vol. XI, 371-408. Redwood City, CA: Addison-Wesley.
12. Rumelhart, DE, Hinton, GE, and Williams, RJ (1986) Learning representations by back-propagating errors. Nature, 323, 533-536. (Pubitemid 16025374)
13. Sutton, R.S. (1988) Learning to predict by the methods of temporal differences. Mach. Learning 3, 9-44.
14. Tesauro, Gerald (1992), Practical Issues in Temporal Difference Learning, Machine Learning 8, 257-277.
15. Tesauro, Gerald (1995) Temporal Difference Learning and TDGammon, Communications of the ACM, March 1995, 38(3):58-68.
16. Tesauro (1989): "Connectionist learning of expert preferences by comparison training", Advances in NIPS 1, 99-106.
17. Backgammon Varieties (2004) http://www.bkgm.com/rgb/rgb.cgi?view+846.
18. Watson RA & Pollack JB, (2001), "Coevolutionary Dynamics in a Minimal Substrate", in GECCO-2001: Proceedings of the Genetic and Evolutionary Computation Conference. Spector, L, et al, editors. Morgan Kaufmann, 2001.