Heterogeneous Data Fusion for an Adaptive Training in Informed Virtual Environment
Résumé
This paper presents an informed virtual environment (environment including knowledge-based models and providing an action/perception coupling) for fluvial navigation training. We add an automatic guide to a driving ship simulator by displaying multimodal aids adapted to human perception for trainees. To this end, a decision-making module determines the most appropriate aids according to heterogeneous data coming from observations of the learner (his/her mistakes, the risks taken, his/her state determined by using physiological sensors, etc.). The Dempster-Shafer theory is used to merge these uncertain data. The purpose of the whole system is to manage the training almost autonomously in order to relieve trainers from controlling the whole training simulation. We intend to demonstrate the relevance of taking the learner's state into account and the relevance of the heterogeneous data fusion with the Dempster-Shafer theory for decision-making about the best learner guiding. First results, obtained with a predefined set of data, show that our decision-making module is able to propose a guiding well-adapted to the trainees, even in complex situations with uncertain data.