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More or Less? Two Approaches to Evolving Game-Playing Strategies

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Genetic Programming Theory and Practice X

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

Abstract

We present two opposing approaches to the evolution of game strategies, one wherein a minimal amount of domain expertise is injected into the process, the other infusing the evolutionary setup with expertise in the form of domain heuristics. We show that the first approach works well for several popular board games, while the second produces top-notch solvers for the hard game of FreeCell.

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Notes

  1. 1.

    Note that since we are using DFS and not BFS we do not find all such states.

References

  • Aler R, Borrajo D, Isasi P (2002) Using genetic programming to learn and improve knowledge. Artificial Intelligence 141(1–2):29–56

    Article  MATH  Google Scholar 

  • Azaria Y, Sipper M (2005) GP-gammon: Genetically programming backgammon players. Genetic Programming and Evolvable Machines 6(3):283–300, DOI doi:10.1007/s10710-005-2990-0, URL http://www.cs.bgu.ac.il/~sipper/papabs/gpgammon.pdf, published online: 12 August 2005

  • Benbassat A, Sipper M (2010) Evolving lose-checkers players using genetic programming. In: IEEE Conference on Computational Intelligence and Game, IT University of Copenhagen, Denmark, pp 30–37, DOI doi:10.1109/ITW.2010.5593376, URL http://game.itu.dk/cig2010/proceedings/papers/cig10-005-011.pdf

  • Benbassat A, Sipper M (2012) Evolving competent board game players for mutiple games with little domain knowledge (in preparation)

    Google Scholar 

  • Berlekamp ER, Conway JH, Guy RK (1982) Winning Ways for your Mathematical Plays. Academic Press, New York, NY, USA

    MATH  Google Scholar 

  • Burke EK, Hyde M, Kendall G, Ochoa G, Ozcan E, Woodward JR (2010) A classification of hyper-heuristic approaches. In: Gendreau M, Potvin J (eds) Handbook of Meta-Heuristics 2nd Edition, Springer, pp 449–468

    Google Scholar 

  • Elyasaf A, Hauptman A, Sipper M (2011) GA-FreeCell: Evolving Solvers for the Game of FreeCell. In: Krasnogor N, et al (eds) GECCO ’11: Proceedings of the 13th annual conference on Genetic and evolutionary computation, ACM, Dublin, Ireland, pp 1931–1938, DOI doi:10.1145/ 2001576.2001836

    Google Scholar 

  • Elyasaf A, Hauptman A, Sipper M (2012) Evolutionary design of freecell solvers (submitted)

    Google Scholar 

  • Hauptman A, Sipper M (2005) GP-endchess: Using genetic programming to evolve chess endgame players. In: Keijzer M, Tettamanzi A, Collet P, van Hemert JI, Tomassini M (eds) Proceedings of the 8th European Conference on Genetic Programming, Springer, Lausanne, Switzerland, Lecture Notes in Computer Science, vol 3447, pp 120–131, DOI doi:10.1007/b107383, URL http://www.cs.bgu.ac.il/~sipper/papabs/eurogpchess-final.pdf

  • Hauptman A, Sipper M (2007) Evolution of an efficient search algorithm for the mate-in-N problem in chess. In: Ebner M, O’Neill M, Ekárt A, Vanneschi L, Esparcia-Alcázar AI (eds) Proceedings of the 10th European Conference on Genetic Programming, Springer, Valencia, Spain, Lecture Notes in Computer Science, vol 4445, pp 78–89, DOI doi:10. 1007/978-3-540-71605-1-8

    Google Scholar 

  • Hauptman A, Elyasaf A, Sipper M, Karmon A (2009) GP-rush: using genetic programming to evolve solvers for the rush hour puzzle. In: Raidl G, Rothlauf F, Squillero G, Drechsler R, Stuetzle T, Birattari M, Congdon CB, Middendorf M, Blum C, Cotta C, Bosman P, Grahl J, Knowles J, Corne D, Beyer HG, Stanley K, Miller JF, van Hemert J, Lenaerts T, Ebner M, Bacardit J, O’Neill M, Di Penta M, Doerr B, Jansen T, Poli R, Alba E (eds) GECCO ’09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, ACM, Montreal, pp 955–962, DOI doi:10.1145/1569901.1570032

    Google Scholar 

  • Hauptman A, Elyasaf A, Sipper M (2010) Evolving hyper heuristic-based solvers for Rush Hour and FreeCell. In: SoCS ’10: Proceedings of the 3rd Annual Symposium on Combinatorial Search (SoCS 2010), pp 149–150

    Google Scholar 

  • Heineman GT (2009) Algorithm to solve FreeCell solitaire games. Broadcast. oreilly. com/2009/01/january-column-graph-algorithm.html. Blog column associated with the book “Algorithms in a Nutshell book,” by G. T. Heineman, G. Pollice, and S. Selkow, O’Reilly Media, 2008

    Google Scholar 

  • Helmert M (2003) Complexity results for standard benchmark domains in planning. Artificial Intelligence 143(2):219–262

    Article  MathSciNet  MATH  Google Scholar 

  • Hillis WD (1992) Co-evolving parasites improve simulated evolution as an optimization procedure. In: Langton CG, Taylor CE, Farmer JD, Rasmussen S (eds) Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, vol X, Addison-Wesley, Santa Fe Institute, New Mexico, USA, pp 313–324

    Google Scholar 

  • Hlynka M, Schaeffer J (2006) Automatic generation of search engines. In: Advances in Computer Games, pp 23–38

    Google Scholar 

  • des Jardins D (1996) Dodgem: Internet discussion. URL http://www.ics.uci.edu/~eppstein/cgt/dodgem.html

  • Lee KF, Mahajan S (1990) The development of a world class othello program. Artificial Intelligence 43(1):21–36, DOI DOI:10.1016/ 0004-3702(90)90068-B

    Google Scholar 

  • Moriarty DE, Miikkulainen R (1995) Discovering complex (othello) strategies through evolutionary neural networks. Connection Science 7(3):195–210

    Article  Google Scholar 

  • Rosenbloom PS (1982) A world-championship-level othello program. Artificial Intelligence 19(3):279–320, DOI DOI:10.1016/0004-3702(82) 90003-0

    Google Scholar 

  • Samadi M, Felner A, Schaeffer J (2008) Learning from multiple heuristics. In: Fox D, Gomes CP (eds) Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI 2008), AAAI Press, pp 357–362

    Google Scholar 

  • Schaeffer J, et al (2007) Checkers is solved. Science 317(5844):1518–1522

    Article  MathSciNet  MATH  Google Scholar 

  • Sipper M (2011) Evolved to Win. Lulu, URL http://www.lulu.com/product/ebook/evolved-to-win/18702814, available at http://www.lulu.com/

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Acknowledgements

This research was supported by the Israel Science Foundation (grant no. 123/11). Achiya Elyasaf is partially supported by the Lynn and William Frankel Center for Computer Sciences.

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Authors and Affiliations

  1. Department of Computer Science, Ben-Gurion University, Beer-Sheva, 84105, Israel

    Amit Benbassat, Achiya Elyasaf & Moshe Sipper

Authors
  1. Amit Benbassat
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  2. Achiya Elyasaf
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  3. Moshe Sipper
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Corresponding author

Correspondence to Amit Benbassat .

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Editors and Affiliations

  1. Center for the Study of Complex Systems, University of Michigan, West Hall 323, Ann Arbor, 48109, Michigan, USA

    Rick Riolo

  2. Evolved Analytics Europe BVBA, Vossenlaan, Beerse, 2340, Belgium

    Ekaterina Vladislavleva

  3. , Department of Biochemistry and Molecular, The Pennsylvania State University, Wartik Laboratory, University Park, 16802, Pennsylvania, USA

    Marylyn D Ritchie

  4. , Institute for Quantitative, Dartmouth Medical School, Medical Center Drive, Lebanon, 03756, New Hampshire, USA

    Jason H. Moore

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Benbassat, A., Elyasaf, A., Sipper, M. (2013). More or Less? Two Approaches to Evolving Game-Playing Strategies. In: Riolo, R., Vladislavleva, E., Ritchie, M., Moore, J. (eds) Genetic Programming Theory and Practice X. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6846-2_12

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  • DOI: https://doi.org/10.1007/978-1-4614-6846-2_12

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