Collective Strategies for Virtual Environments: Modelling Ways to Deal with the Uncertainty

  • L. CastilloEmail author
  • C. López
  • M. Bedia
  • F. López
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 373)


Videogames offer new challenges and excellent application domains for AI technology and research. This paper shows a work on ant algorithms optimizing of collective strategies in a videogame environment. In general, ant algorithms are those that taking inspiration from the observation of ant colonies foraging behavior have developed optimization meta-heuristics. In the first part of the paper, we relate the strengths of ant strategies with the well-known exploration-exploitation problem. In the second part, a particular model on how to optimize group strategies in collective tasks is analyzed in light of the ideas previously examined. Finally, we will show how these ideas can constitute an improved stage in the problem of designing non-player characters in future videogames.


videogames exploration-exploitation dilemma ant optimization 


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  1. 1.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall (December 2002)Google Scholar
  2. 2.
    Kasap, Z., Magnenat-Thalman, N.: Intelligent virtual humans with autonomy and personality: State-of-the-art. In: Intelligent Decision Technologies. IOS Press (2007)Google Scholar
  3. 3.
    Stork, H.-G.: Report on the Workshop “Future Trends in Artificial Cognitive Systems”. In: Frankfurt Conference Centre, December 9-10. ECVISION (2004)Google Scholar
  4. 4.
    Hoffman, R.R., Klein, G., Laughery, K.R.: The State of Cognitive Systems Engineering. IEEE Intelligent Systems 17(1), 73–75 (2002)CrossRefGoogle Scholar
  5. 5.
    Woods, D.D., Hollnagel, E.: Joint cognitive systems: Patterns in cognitive systems engineering. Taylor & Francis (2006)Google Scholar
  6. 6.
    Du Boulay, B., Rebolledo-Mendez, G., et al.: Motivationally intelligent systems: Three questions. In: Second International Conference on Innovations in Learning for the Future, Future e-Learning 2008, Istanbul, pp. 1–10 (2008)Google Scholar
  7. 7.
    Cole, N., Louis, S.J., Miles, C.: Using a genetic algorithm to tune first-person shooter bots. In: Proceedings of the Congress on Evolutionary Computation, vol. 1, pp. 139–145. IEEE, Piscataway (2004)Google Scholar
  8. 8.
    Kendall, G., Spoerer, K.: Scripting the game of Lemmings with a genetic algorithm. In: Proceedings of the 2004 Congress on Evolutionary Computation, vol. 1, pp. 117–124. IEEE, Piscataway (2004)Google Scholar
  9. 9.
    Alliot, J.M., Durand, N.: A genetic algorithm to improve an Othello program. Artificial Evolution, 307–319 (1995)Google Scholar
  10. 10.
    Sims, K.: Evolving virtual creatures. In: Computer Graphics Proceedings. Annual Conference Series 1994. SIGGRAPH 1994 Conference Proceedings, pp. 15–22. ACM, New York (1994)Google Scholar
  11. 11.
    Berger-Tal, O., Nathan, J., Meron, E., Saltz, D.: The Exploration-Exploitation Dilemma: A Multidisciplinary Framework. PLoS ONE 9(4), e95693 (2014), doi:10.1371/journal.pone.0095693Google Scholar

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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Engineering Systems Dpt.University of CaldasManizalesColombia
  2. 2.Plastic and Physical ExpressionUniversity of ZaragozaZaragozaSpain
  3. 3.Computer Science Dpt.University of ZaragozaZaragozaSpain

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