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Evidential Network with Conditional Belief Functions for an Adaptive Training in Informed Virtual Environment

  • Loïc Fricoteaux
  • Indira Thouvenin
  • Jérôme Olive
  • Paul George
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

Simulators have been used for many years to learn driving, piloting, steering, etc. but they often provide the same training for each learner, no matter his/her performance. In this paper, we present the GULLIVER system, which determines the most appropriate aids to display for learner guiding in a fluvial-navigation training simulator. GULLIVER is a decision-making system based on an evidential network with conditional belief functions. This evidential network allows graphically representing inference rules on uncertain data coming from learner observation. Several sensors and a predictive model are used to collect these data about learner performance. Then the evidential network is used to infer in real time the best guiding to display to learner in informed virtual environment.

Keywords

Virtual Reality Virtual Environment Augmented Reality Belief Function Conditional Belief 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Loïc Fricoteaux
    • 1
  • Indira Thouvenin
    • 1
  • Jérôme Olive
    • 1
  • Paul George
    • 1
  1. 1.Heudiasyc Laboratory UMR CNRS 6599CompiégneFrance

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