Evolving Chess-like Games Using Relative Algorithm Performance Profiles

  • Jakub Kowalski
  • Marek Szykuła
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9597)


We deal with the problem of automatic generation of complete rules of an arbitrary game. This requires a generic and accurate evaluating function that is used to score games. Recently, the idea that game quality can be measured using differences in performance of various game-playing algorithms of different strengths has been proposed; this is called Relative Algorithm Performance Profiles.

We formalize this method into a generally application algorithm estimating game quality, according to some set of model games with properties that we want to reproduce. We applied our method to evolve chess-like boardgames. The results show that we can obtain playable and balanced games of high quality.


Procedural content generation Evolutionary algorithms Relative algorithm performance profiles Simplified board games General game playing 


  1. 1.
    Shaker, N., Togelius, J., Nelson, M.J.: Procedural Content Generation in Games: A Textbook and an Overview of Current Research. Springer, Heidelberg (2015)Google Scholar
  2. 2.
    Nelson, M.J., Mateas, M.: Towards automated game design. In: AI* IA 2007: Artificial Intelligence and Human-Oriented Computing, pp. 626–637 (2007)Google Scholar
  3. 3.
    Togelius, J., Nelson, M.J., Liapis, A.: Characteristics of generatable games. In: Proceedings of the FDG Workshop on Procedural Content Generation (2014)Google Scholar
  4. 4.
    Zook, A., Riedl, M.O.: Automatic game design via mechanic generation. In: AAAI, pp. 530–537 (2014)Google Scholar
  5. 5.
    Browne, C., Maire, F.: Evolutionary game design. IEEE Trans. Comput. Intell. AI Games 2(1), 1–16 (2010)CrossRefGoogle Scholar
  6. 6.
    Pell, B.: METAGAME in symmetric chess-like games. In: Programming in Artificial Intelligence: The Third Computer Olympiad (1992)Google Scholar
  7. 7.
    Font, J.M., Mahlmann, T., Manrique, D., Togelius, J.: A card game description language. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 254–263. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Mahlmann, T., Togelius, J., Yannakakis, G.: Modelling and evaluation of complex scenarios with the strategy game description language. In: CIG, pp. 174–181 (2011)Google Scholar
  9. 9.
    Togelius, J., Schmidhuber, J.: An experiment in automatic game design. In: CIG, pp. 111–118 (2008)Google Scholar
  10. 10.
    Cook, M., Colton, S.: Multi-faceted evolution of simple arcade games. In: CIG, pp. 289–296 (2011)Google Scholar
  11. 11.
    Schaul, T.: A video game description language for model-based or interactive learning. In: CIG, pp. 1–8 (2013)Google Scholar
  12. 12.
    Nielsen, T., Barros, G., Togelius, J., Nelson, M.: Towards generating arcade game rules with VGDL. In: CIG, pp. 185–192 (2015)Google Scholar
  13. 13.
    Nielsen, T., Barros, G., Togelius, J., Nelson, M.: General video game evaluation using relative algorithm performance profiles. In: Mora, A.M., Squillero, G. (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, vol. 9028, pp. 369–380. Springer, Switzerland (2015)Google Scholar
  14. 14.
    Björnsson, Y.: Learning rules of simplified boardgames by observing. In: ECAI, FAIA, vol. 242, pp. 175–180. IOS Press (2012)Google Scholar
  15. 15.
    Genesereth, M., Love, N., Pell, B.: General game playing: overview of the AAAI competition. AI Mag. 26, 62–72 (2005)Google Scholar
  16. 16.
    Love, N., Hinrichs, T., Haley, D., Schkufza, E., Genesereth, M.: General game playing: game description language specification. Technical report, Stanford Logic Group (2008)Google Scholar
  17. 17.
    Thielscher, M.: A general game description language for incomplete information games. In: AAAI, pp. 994–999 (2010)Google Scholar
  18. 18.
    Kowalski, J., Kisielewicz, A.: Game description language for real-time games. In: GIGA, pp. 23–30 (2015)Google Scholar
  19. 19.
    Perez, D., Samothrakis, S., Togelius, J., Schaul, T., Lucas, S., Couëtoux, A., Lee, J., Lim, C., Thompson, T.: The 2014 general video game playing competition. In: CIG (2015)Google Scholar
  20. 20.
    Pitrat, J.: Realization of a general game-playing program. In: IFIP Congress, pp. 1570–1574 (1968)Google Scholar
  21. 21.
    Jaśkowski, W., Liskowski, P., Szubert, M., Krawiec, K.: Improving coevolution by random sampling. In: GECCO, pp. 1141–1148 (2013)Google Scholar
  22. 22.
    Szubert, M., Jaśkowski, W., Liskowski, P., Krawiec, K.: The role of behavioral diversity and difficulty of opponents in coevolving game-playing agents. In: Mora, A.M., Squillero, G. (eds.) Applications of Evolutionary Computation. Lecture Notes in Computer Science, vol. 9028, pp. 394–405. Springer, Switzerland (2015)Google Scholar
  23. 23.
    Kowalski, J., Kisielewicz, A.: Testing general game players against a simplified boardgames player using temporal-difference learning. In: CEC, pp. 1466–1473. IEEE (2015)Google Scholar
  24. 24.
    Gregory, P., Björnsson, Y., Schiffel, S.: The GRL system: learning board game rules with piece-move interactions. In: GIGA, pp. 55–62 (2015)Google Scholar
  25. 25.
    The Chess Variant Pages.
  26. 26.
    Droste, S., Fürnkranz, J.: Learning the piece values for three chess variants. Int. Comput. Games Assoc. J. 31(4), 209–233 (2008)Google Scholar
  27. 27.
    Hom, V., Marks, J.: Automatic design of balanced board games. In: AIIDE, pp. 25–30 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of WrocławWrocławPoland

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