Summary
We present a description and initial results of a computer code that coevolves Fuzzy Logic rules to play a two-sided zero-sum competitive game. It is based on the TEMPO Military Planning Game that has been used to teach resource allocation to over 20,000 students over the past 40 years. No feasible algorithm for optimal play is known. The coevolved rules, when pitted against human players, usually win the first few competitions. For reasons not yet understood, the evolved rules (found in a symmetrical competition) place no value on information concerning the play of the opponent.
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References
W. Anderson, “Processing Graph Method (PGMT) User’s Manual”, US Naval Research Laboratory, October 2002.
R. Axelrod, “Evolution of Strategies in the Iterated Prisoner’s Dilemma”, in “Genetic Algorithms and Simulated Annealing”, L. Davis, ed., Pitman, London, pp. 32–41, 1987.
K. Chellapilla and D.B. Fogel, “Evolution, neural networks, games, and intelligence”, in Proceedings of the IEEE, Vol. 87, No. 9, pp. 1471–1496, 1999.
K. Chellapilla and D.B. Fogel, “Evolving neural networks to play checkers without expert knowledg””, in IEEE Transactions on Neural Networks, Vol. 10, No. 6, pp. 1382–1391, 1999.
K. Chellapilla and D.B. Fogel, “Evolving an expert checkers playing program without using human expertise”, in IEEE Transactions on Evolutionary Computation, Vol. 5, No. 4, pp. 422–428, 2001.
D. Cliff and G. F. Miller (1996). CoEvolution of Neural Networks for Con-trol of Pursuit and Evasion. University of Sussex, U.K. [Online]. Available: http://www.cogs.susx.ac.uk/users/davec/pe.html
P.J. Darwen and X. Yao, “Why More Choices Cause Less Cooperation in Iterated Prisoner’s Dilemma”, in Proceedings of the 2001 Congress on Evolutionary Computation, IEEE, Piscataway, NJ, pp. 987–994.
D. Floreano. and S. Nolfi, “God Save the Red Queen! Competition in Co-evolutionary Robotics,” in Genetic Programming 1997, J.R. Koza, K. Deb, M. Dorigo, D.B. Fogel, M. Garzon, H. Iba, and R.L. Riolo, eds., Morgan Kaufmann, San Mateo, CA, pp.398–406, 1997.
D.B. Fogel, “Evolving Behaviors in the Iterated Prisoner’s Dilemma”, in Evolutionary Computation, Vol. 1, No. 1, pp. 77–97, 1993.
D.B. Fogel, “Evolutionary Computation: Toward a New Philosophy of Machine Intelligence”, in IEEE Press, Piscataway, NJ, 1995.
D.B. Fogel, “Blondie 24: Playing At The Edge of AI”, Morgan Kaufmann, San Francisco, CA, 2002.
D.B. Fogel and T.J. Hays, “New results in evolving strategies in chess”, in Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, Vol. 5200, B. Bosacchi, D.B. Fogel, and J.C. Bezdek (chairs), SPIE, Bellingham, WA, pp. 56–63, 2003.
P.G. Harrald and D.B. Fogel, “Evolving Continuous Behaviors in the Iterated Prisoner’s Dilemma”, in BioSystems, Vol. 37, pp. 135–145, 1996.
W.D. Hillis, “Co-evolving Parasites Improve Simulated Evolution as an Optimization Pro-cedure”, in “Artificial Life II”, C.G. Langton, C. Taylor, J.D. Farmer, and S. Rasmussen, eds., Addison-Wesley, Reading, MA, pp. 313–324, 1992.
D. Kaplan, “Introduction to the Processing Graph Method”, U.S. Naval Research Laboratory, Mar. 1997.
J.R. Koza, “Genetic Programming”, Cambridge, MA: MIT Press, 1992.
D.E. Knuth, “Sorting and Searching”, Vol.3, in “The Art of Computer Programming”, Addison-Wesley, New York, NY, 1973.
R. Le Riche, C. Knopf-Lenoir, and R.T. Haftka, “A Segregated Genetic Algorithm for Constrained Structural Optimization”. in Proceedings of the Sixth International Confer-ence on Genetic Algorithms, L.J. Eshelman, ed., Morgan Kaufmann, San Mateo, CA, pp.558–565, 1995.
D. Lovallo and D. Kahneman, “Delusions of Success,” Harvard Business Review, vol. 81, no. 7, pp. 57–63, July 2003.
Z. Michalewicz and G. Nazhiyath, “GENOCOP III: A Coevolutionary Algorithm for Numerical Optimization Problems with Nonlinear Constraints,” in Proceedings of the 1995 IEEE Conference on Evolutionary Computation. IEEE Press, Piscataway, NJ, pp.647–651, 1995.
J. Paredis, “Co-Evolutionary Constraint Satisfaction,” in Proceedings of the 3rd Conference on Parallel Problem Solving from Nature, Y. Davidor, H.-P. Schwefel, and R. Man-ner, eds., Lecture Notes in Computer Science, vol.866, Springer, Berlin, pp.46–55, 1994.
J. Paredis,. “The Symbiotic Evolution of Solutions and Their Representations,” in Proceedings of the Sixth International Conference on Genetic Algorithms, L.J. Eshelman, ed., Morgan Kaufmann, San Mateo, CA, pp.359–365,. 1995.
C. Reynolds, “Competition, Coevolution and the Game of Tag”, in Proceedings of Artificial Life IV, R. Brooks and P. Maes, eds., MIT Press, Cambridge, MA, pp. 56–69, 1994.
Peter Schwartz, The Art of the Long View: Planning for the Future in an Uncertain World. New York: Currency/Doubleday, 1991.
A.V. Sebald and J. Schlenzig, “Minimax Design of Neural-net Controllers for Uncertain Plants”, IEEE Transactions on Neural Networks, Vol.5, No. l, pp. 73–82, 1994.
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Johnson, R.W., Melich, M.E., Michalewicz, Z., Schmidt, M. (2005). Coevolutionary Processes for Strategic Decisions. In: Monitoring, Security, and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32370-8_6
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DOI: https://doi.org/10.1007/3-540-32370-8_6
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