Automated Game Balancing in Ms PacMan and StarCraft Using Evolutionary Algorithms

  • Mihail MorosanEmail author
  • Riccardo Poli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)


Games, particularly online games, have an ongoing requirement to exhibit the ability to react to player behaviour and change their mechanics and available tools to keep their audience both entertained and feeling that their strategic choices and in-game decisions have value. Game designers invest time both gathering data and analysing it to introduce minor changes that bring their game closer to a state of balance, a task with a lot of potential that has recently come to the attention of researchers. This paper first provides a method for automating the process of finding the best game parameters to reduce the difficulty of Ms PacMan through the use of evolutionary algorithms and then applies the same method to a much more complex and commercially successful PC game, StarCraft, to curb the prowess of a dominant strategy. Results show both significant promise and several avenues for future improvement that may lead to a useful balancing tool for the games industry.


Evolutionary algorithms Game balance Automation PacMan StarCraft 


  1. 1.
    StarCraft AI, the StarCraft BroodWar Resource for Custom AIs.
  2. 2.
    AIIDE: 2015 AIIDE StarCraft AI Competition Report (2015).
  3. 3.
    Back, T., Fogel, D.B., Michalewicz, Z.: Handbook of Evolutionary Computation. IOP Publishing Ltd., Bristol (1997)CrossRefzbMATHGoogle Scholar
  4. 4.
    Beyer, M., Agureikin, A., Anokhin, A., Laenger, C., Nolte, F., Winterberg, J., Renka, M., Rieger, M., Pflanzl, N., Preuss, M., Volz, V.: An Integrated process for game balancing. In: IEEE Conference on Computational Intelligence and Games (2016)Google Scholar
  5. 5.
    Burgun, K.: Understanding Balance in Video Games (2011).
  6. 6.
  7. 7.
    Cagnoni, S., Dobrzeniecki, A.B., Poli, R., Yanch, J.C.: Genetic algorithm-based interactive segmentation of 3D medical images. Image Vis. Comput. 17(12), 881–895 (1999)CrossRefGoogle Scholar
  8. 8.
    Chen, H., Mori, Y., Matsuba, I.: Solving the balance problem of on-line role-playing games using evolutionary algorithms. J. Softw. Eng. Appl. 05(08), 574–582 (2012). Scholar
  9. 9.
    Cincotti, A., Iida, H., Cincotti, A., Iida, H.: Outcome uncertainty and interestedness in game-playing: a case study using synchronized hex. New Math. Nat. Comput. (NMNC) 02(02), 173–181 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Coxe, C.: ZZZKBot (2015).
  11. 11.
    David, O.E., van den Herik, H.J., Koppel, M., Netanyahu, N.S.: Genetic algorithms for evolving computer chess programs. IEEE Trans. Evol. Comput. 18(5), 779–789 (2014)CrossRefGoogle Scholar
  12. 12.
    Davis, L.: Handbook of Genetic Algorithms (1991)Google Scholar
  13. 13.
    DeLooze, L.L., Viner, W.R.: Fuzzy Q-learning in a nondeterministic environment: developing an intelligent Ms. Pac-Man agent. In: 2009 IEEE Symposium on Computational Intelligence and Games, pp. 162–169. IEEE, September 2009.
  14. 14.
    Garcia-Sanchez, P., Tonda, A., Mora, A.M., Squillero, G., Merelo, J.: Towards automatic StarCraft strategy generation using genetic programming. In: 2015 IEEE Conference on Computational Intelligence and Games (CIG), pp. 284–291. IEEE, August 2015.
  15. 15.
    Goldberg, D.E., et al.: Genetic Algorithms in Search Optimization and Machine Learning, vol. 412. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  16. 16.
    Linden, D.S., Altshuler, E.E.: Automating wire antenna design using genetic algorithms. Microw. J. 39(3), 74–81 (1996)Google Scholar
  17. 17.
    Lucas, S.: Evolving a neural network location evaluator to play Ms. Pac-Man. In: IEEE Symposium on Computational Intelligence and Games, pp. 203–210 (2005)Google Scholar
  18. 18.
    Mahlmann, T., Togelius, J., Yannakakis, G.N.: Evolving card sets towards balancing dominion. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–8 (2012)Google Scholar
  19. 19.
    MasterOfChaos: Chaoslauncher (2011).
  20. 20.
  21. 21.
    Pepels, T., Winands, M.H.M., Lanctot, M.: Real-time Monte Carlo tree search in Ms Pac-Man. IEEE Trans. Comput. Intell. AI Games 6(3), 245–257 (2014). Scholar
  22. 22.
    Preble, S., Lipson, M., Lipson, H.: Two-dimensional photonic crystals designed by evolutionary algorithms. Appl. Phys. Lett. 86(6), 61111 (2005)CrossRefGoogle Scholar
  23. 23.
    Ramos, J.I.E., Vázquez, R.A.: Locating seismic-sense stations through genetic algorithms. Proc. GECCO 11, 941–948 (2011)Google Scholar
  24. 24.
  25. 25.
  26. 26.
    Shelton, L.: Implementation of high-level strategy formulating AI in Ms Pac-Man. Technical report (2013).
  27. 27.
  28. 28.
    Thompson, T., McMillan, L., Levine, J., Andrew, A.: An evaluation of the benefits of look-ahead in Pac-Man. In: 2008 IEEE Symposium Computational Intelligence and Games, pp. 310–315. IEEE (2008)Google Scholar
  29. 29.
    Volz, V., Rudolph, G., Naujoks, B.: Demonstrating the Feasibility of Automatic Game Balancing, March 2016.
  30. 30.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of EssexColchesterUK

Personalised recommendations