Decision Tree-Based Algorithms for Implementing Bot AI in UT2004

  • Antonio J. Fernández Leiva
  • Jorge L. O’Valle Barragán
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

This paper describes two different decision tree-based approaches to obtain strategies that control the behavior of bots in the context of the Unreal Tournament 2004. The first approach follows the traditional process existing in commercial videogames to program the game artificial intelligence (AI), that is to say, it consists of coding the strategy manually according to the AI programmer’s experience with the aim of increasing player satisfaction. The second approach is based on evolutionary programming techniques and has the objective of automatically generating the game AI. An experimental analysis is conducted in order to evaluate the quality of our proposals. This analysis is executed on the basis of two fitness functions that were defined intuitively to provide entertainment to the player. Finally a comparison between the two approaches is done following the subjective evaluation principles imposed by the “2k bot prize” competition.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Johnson, D., Wiles, J.: Computer games with intelligence. In: FUZZ-IEEE, pp. 1355–1358 (2001)Google Scholar
  2. 2.
    Millington, I.: Artificial Intelligence for Games. In: Interactive 3D Technology. Morgan Kaufmann, San Francisco (2006)Google Scholar
  3. 3.
    Buckland, M.: AI Techniques for Game Programming. Premier Press (2002)Google Scholar
  4. 4.
    Bourg, D., Seemann, G.: AI for Game Developers. O’Reilly, Sebastopol (2004)Google Scholar
  5. 5.
    Miikkulainen, R., Bryant, B.D., Cornelius, R., Karpov, I.V., Stanley, K.O., Yong, C.H.: Computational intelligence in games. In: Computational Intelligence: Principles and Practice, pp. 155–191. IEEE Computational Intelligence Society, Piscataway (2006)Google Scholar
  6. 6.
    Turing, A.: Computing machinery and intelligence. Mind 59, 433–460 (1950)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATHGoogle Scholar
  8. 8.
    Sweetser, P.: How to build evolutionary algorithms for games. In: AI Game Programming Wisdom 2, pp. 627–638. Charles River Media, Inc. (2003)Google Scholar
  9. 9.
    Kim, K.J., Cho, S.-B.: Evolutionary algorithms for board game players with domain knowledge. In: Baba, N., Jain, L.C., Handa, H. (eds.) Advanced Intelligent Paradigms in Computer Games, pp. 71–89. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Fogel, D.B.: Evolving a checkers player without relying on human experience. Intelligence 11(2), 20–27 (2000)CrossRefGoogle Scholar
  11. 11.
    Chellapilla, K., Fogel, D.B.: Evolving an expert checkers playing program without using human expertise. IEEE Trans. Evolut. Comput. 5(4), 422–428 (2001)CrossRefGoogle Scholar
  12. 12.
    Wittkamp, M., Barone, L.: Evolving adaptive play for the game of spoof using genetic programming. In: Louis, S.J., Kendall, G. (eds.) IEEE Symposium on Computational Intelligence and Games (CIG 2006), University of Nevada, Reno, campus in Reno/Lake Tahoe, pp. 164–172. IEEE, Los Alamitos (2006)CrossRefGoogle Scholar
  13. 13.
    Pollack, J.B., Blair, A.D.: Co-evolution in the successful learning of backgammon strategy. Machine Learning 32(3), 225–240 (1998)CrossRefMATHGoogle Scholar
  14. 14.
    Ong, C., Quek, H., Tan, K., Tay, A.: Discovering chinese chess strategies through coevolutionary approaches. In: IEEE Symposium on Computational Intelligence and Games (CIG 2007), pp. 360–367. IEEE, Los Alamitos (2007)CrossRefGoogle Scholar
  15. 15.
    Fernández, A.J., Jiménez, J.G.: Action games: Evolutive experiences. In: Reusch, B. (ed.) Computational Intelligence, Theory and Applications, International Conference 8th Fuzzy Days. AISC, vol. 33, pp. 487–501. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Spronck, P., Sprinkhuizen-Kuyper, I., Postma, E.: Improving opponent intelligence through offline evolutionary learning. International Journal of Intelligent Games & Simulation 2(1), 20–27 (2003)Google Scholar
  17. 17.
    Mora, A.M., Montoya, R., Guervós, J.J.M., Sánchez, P.G., Castillo, P.Á., Laredo, J.L.J., García, A.I.M., Espacia, A.: Evolving bot AI in unrealtm. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 171–180. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Esparcia-Alcázar, A.I., García, A.I.M., Mora, A., Guervós, J.J.M., García-Sánchez, P.: Controlling bots in a first person shooter game using genetic algorithms. In: CEC 2010, Barcelona, Spain, pp. 1–8. IEEE, Los Alamitos (2010)Google Scholar
  19. 19.
    Andrade, G., Ramalho, G., Gomes, A., Corruble, V.: Dynamic game balancing: an evaluation of user satisfaction. In: AIIDE Artificial Intelligence and Interactive Digital Entertainment, pp. 3–8. AAI Press (2006)Google Scholar
  20. 20.
    Yannakakis, G.N.: How to model and augment player satisfaction: A review. In: 1st Workshop on Child, Computer and Interaction, Crete. ACM Press, New York (2008) (Invited paper)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio J. Fernández Leiva
    • 1
  • Jorge L. O’Valle Barragán
    • 1
  1. 1.Dept. Lenguajes y Ciencias de la Computación, ETSI InformáticaCampus de Teatinos, Universidad de MálagaMálagaSpain

Personalised recommendations