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Genetic Programming and Evolvable Machines

, Volume 9, Issue 4, pp 281–294 | Cite as

Evolving strategy for a probabilistic game of imperfect information using genetic programming

  • Wojciech Jaśkowski
  • Krzysztof KrawiecEmail author
  • Bartosz Wieloch
Original Paper

Abstract

We provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. Ant Wars contestants are virtual ants collecting food on a grid board in the presence of a competing ant. BrilliAnt has been evolved through a competitive one-population coevolution using genetic programming and fitnessless selection. In this paper, we detail the evolutionary setup that lead to BrilliAnt’s emergence, assess its direct and indirect human-competitiveness, and describe the behavioral patterns observed in its strategy.

Keywords

Genetic Programming Game Playing Board State Food Piece Popular Benchmark Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors wish to thank the anonymous reviewers for valuable feedback and discussion on this work. This research has been supported by the Ministry of Science and Higher Education grant # N N519 3505 33.

References

  1. 1.
    P.J. Angeline, J.B. Pollack, Competitive environments evolve better solutions for complex tasks, in Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, University of Illinois at Urbana-Champaign, 17–21 July 1993, ed. by S. Forrest (Morgan Kaufmann, 1993), pp. 264–270Google Scholar
  2. 2.
    Y. Azaria, M. Sipper, GP-gammon: genetically programming backgammon players. Genet. Prog. Evol. Mach. 6(3), 283–300 (2005)CrossRefGoogle Scholar
  3. 3.
    M. Buro, Real-time strategy games: a new AI research challenge, in IJCIA, ed. by G. Gottlob, T. Walsh (Morgan Kaufmann, San Francisco, 2003), pp. 1534–1535Google Scholar
  4. 4.
    J.B. Caverlee, A genetic algorithm approach to discovering an optimal blackjack strategy, in Genetic Algorithms and Genetic Programming at Stanford 2000, Stanford Bookstore, Stanford, CA, 94305-3079 USA, June 2000, ed. by J.R. Koza, pp. 70–79Google Scholar
  5. 5.
    F. Corno, E. Sanchez, G. Squillero, On the evolution of corewar warriors, in Proceedings of the 2004 IEEE Congress on Evolutionary Computation, Portland, OR, 20–23 June 2004 (IEEE Press, 2004), pp. 133–138Google Scholar
  6. 6.
    E. de Jong, The maxsolve algorithm for coevolution, in GECCO 2005: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, Washington DC, USA, 25–29 June 2005, ed. by H.-G. Beyer, U.-M. O’Reilly, D.V. Arnold, W. Banzhaf, C. Blum, E.W. Bonabeau, E. Cantu-Paz, D. Dasgupta, K. Deb, J.A. Foster, E.D. de Jong, H. Lipson, X. Llora, S. Mancoridis, M. Pelikan, G.R. Raidl, T. Soule, A.M. Tyrrell, J.-P. Watson, E. Zitzler, vol. 1 (ACM Press, 2005), pp. 483–489Google Scholar
  7. 7.
    E.D. de Jong, A monotonic archive for pareto-coevolution. Evol. Comput. 15(1), 61–93 (2007)CrossRefGoogle Scholar
  8. 8.
    S. Ficici, J. Pollack, A game-theoretic memory mechanism for coevolution, in Genetic and Evolutionary Computation, Chicago, July 2003, ed. by E. Cantú-Paz, J. Foster, K. Deb, D. Davis, R. Roy, U.-M. O’Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta, M. Potter, A.C. Schultz, K. Dowsland, N. Jonoska, J. Miller. Lecture Notes in Computer Science, vol. 2723 (Springer, 2003), pp. 286–297Google Scholar
  9. 9.
    D.B. Fogel, Blondie24: Playing at the Edge of AI (Morgan Kaufmann Publishers Inc., San Francisco, CA, 2002)Google Scholar
  10. 10.
    A. Hauptman, M. Sipper, Evolution of an efficient search algorithm for the mate-in-N problem in chess, in Proceedings of the 10th European Conference on Genetic Programming, vol. 4445 of Lecture Notes in Computer Science, Valencia, Spain, 11–13 Apr. 2007, ed. by M. Ebner, M. O’Neill, A. Ekárt, L. Vanneschi, A.I. Esparcia-Alcázar (Springer, 2007), pp. 78–89Google Scholar
  11. 11.
    W. Jaśkowski, K. Krawiec, B. Wieloch, AntWars Applet, 2007, http://www.cs.put.poznan.pl/kkrawiec/antwars/
  12. 12.
    W. Jaśkowski, K. Krawiec, B. Wieloch, Fitnessless coevolution, in GECCO ’08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, 2008Google Scholar
  13. 13.
    J.R. Koza, Genetic evolution and co-evolution of game strategies, in The International Conference on Game Theory and Its Applications, Stony Brook, New York, 15 July, 1992Google Scholar
  14. 14.
    J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT Press, Cambridge, MA, USA, 1992)zbMATHGoogle Scholar
  15. 15.
    A. Lubberts, R. Miikkulainen, Co-evolving a go-playing neural network, in Coevolution: Turning Adaptive Algorithms upon Themselves, San Francisco, CA, USA, 7 July 2001, ed. by R.K. Belew, H. Juillè, pp. 14–19Google Scholar
  16. 16.
    S. Luke, Genetic programming produced competitive soccer softbot teams for robocup97, in Genetic Programming 1998: Proceedings of the Third Annual Conference, University of Wisconsin, Madison, WI, USA, 22–25 July 1998, ed. by J.R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, R. Riolo (Morgan Kaufmann, 1998), pp. 214–222Google Scholar
  17. 17.
    S. Luke, ECJ Evolutionary Computation System, 2002, http://cs.gmu.edu/eclab/projects/ecj/
  18. 18.
    S. Luke, R.P. Wiegand, When coevolutionary algorithms exhibit evolutionary dynamics, in Workshop on Understanding Coevolution: Theory and Analysis of Coevolutionary Algorithms (at GECCO 2002), ed. by A. Barry (AAAI Press, New York, 2002), pp. 236–241Google Scholar
  19. 19.
    G.A. Monroy, K.O. Stanley, R. Miikkulainen. Coevolution of neural networks using a layered pareto archive, in GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, Seattle, WA, USA, 8–12 July 2006, ed. by M. Keijzer, M. Cattolico, D. Arnold, V. Babovic, C. Blum, P. Bosman, M.V. Butz, C. Coello Coello, D. Dasgupta, S.G. Ficici, J. Foster, A. Hernandez-Aguirre, G. Hornby, H. Lipson, P. McMinn, J. Moore, G. Raidl, F. Rothlauf, C. Ryan, D. Thierens, vol. 1 (ACM Press, 2006), pp. 329–336Google Scholar
  20. 20.
    D.J. Montana, Strongly typed genetic programming. Evol. Comput. 3(2), 199–230 (1995)CrossRefGoogle Scholar
  21. 21.
    J.B. Pollack, A.D. Blair, Co-evolution in the successful learning of backgammon strategy. Mach. Learn. 32(3), 225–240 (1998)zbMATHCrossRefGoogle Scholar
  22. 22.
    C. Reynolds, Competition, coevolution and the game of tag, in Artificial Life IV, Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems, ed. by R.A. Brooks, P. Maes (MIT Press, 1994), pp. 59–69Google Scholar
  23. 23.
    Y. Shichel, E. Ziserman, M. Sipper, GP-robocode: using genetic programming to evolve robocode players, in Proceedings of the 8th European Conference on Genetic Programming, Lausanne, Switzerland, 30 Mar.–1 Apr. 2005, ed. by M. Keijzer, A. Tettamanzi, P. Collet, J.I. van Hemert, M. Tomassini. Lecture Notes in Computer Science, vol. 3447 (Springer, 2005), pp. 143–154Google Scholar
  24. 24.
    M. Sipper, Attaining human-competitive game playing with genetic programming, in Proceedings of the 7th International Conference on Cellular Automata, for Research and Industry, ACRI, Perpignan, France, Sept. 20–23 2006, ed. by S.E. Yacoubi, B. Chopard, S. Bandini. Lecture Notes in Computer Science, vol. 4173 (Springer, Invited Lectures), p. 13 Google Scholar
  25. 25.
    K.C. Smilak, Finding the ultimate video poker player using genetic programming, in Genetic Algorithms and Genetic Programming at Stanford 1999, Stanford Bookstore, Stanford, CA, 94305-3079 USA, 15 Mar. 1999, ed. by J.R. Koza, pp. 209–217Google Scholar
  26. 26.
    K. Stanley, B. Bryant, R. Miikkulainen, Real-time neuroevolution in the NERO video game. IEEE Trans. Evolut. Comput. 9(6), 653–668 (2005)CrossRefGoogle Scholar
  27. 27.
    A.G.B. Tettamanzi, Genetic programming without fitness, in Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28–31, 1996, Stanford University, CA, USA, 28–31 July 1996, ed. by J.R. Koza (Stanford Bookstore, 1996), pp. 193–195Google Scholar
  28. 28.
    D. Whitley, S. Rana, R. Heckendorn, The island model genetic algorithm: on separability, population size and convergence. J. Comput. Inform. Technol. 7(1), 33–47 (1999)Google Scholar
  29. 29.
    M. Wittkamp, L. Barone, Evolving adaptive play for the game of spoof using genetic programming, in Proceedings of the 2006 IEEE Symposium on Computational Intelligence and Games (CIG06), University of Nevada, Reno, campus in Reno/Lake Tahoe, USA, 22–24 May 2006, ed. by S.J. Louis, G. Kendall (IEEE Press, 2006), pp. 164–172Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Wojciech Jaśkowski
    • 1
  • Krzysztof Krawiec
    • 1
    Email author
  • Bartosz Wieloch
    • 1
  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznanPoland

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