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Learning Classifier Systems and Behavioural Animation of Virtual Characters

  • S. Sanchez
  • H. Luga
  • Y. Duthen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4133)

Abstract

Producing intuitive systems for the directing of virtual actors is one of the major objectives of research in virtual animation. So, it is often interesting to conceive systems that enable behavioral animation of autonomous characters, able to correctly fulfill directives from a human user considering their goal and their perception of the virtual environment. Common ways to generate behaviors of such virtual characters use usually determinist algorithm (scripts or automatons [1]). Thus the autonomy of the characters is a fixed routine that cannot adapt to novelty or any situation not previously considered. To make these virtual actors able of adaptation, we propose to combine a behavioral framework (ViBes [2]) and an evolutionist learning system, the Learning Classifier Systems [3]. Using classifiers systems we managed to make a virtual human to learn to select and to cook an aliment in order to eat something. The association of ViBes framework and two trained classifiers systems produced the following real time animation (fig. [1]) in a dynamic virtual environment.

References

  1. 1.
    Donikian, S.: Hpts: a behaviour modelling language for autonomous agents. In: Fifth International Conference on Autonomous Agents, pp. 401–408 (May 2001)Google Scholar
  2. 2.
    Sanchez, S., Luga, H., Duthen, Y., Balet, O.: Bringing autonomy to virtual characters. In: ISSADS, pp. 401–408 (2004)Google Scholar
  3. 3.
    Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. Sanchez
    • 1
  • H. Luga
    • 1
  • Y. Duthen
    • 1
  1. 1.Toulouse Research Institute for Informatics (IRIT)Université Toulouse 1ToulouseFrance

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