Directed Evidential Networks with Conditional Belief Functions
Evidential Network with Conditional Belief Functions for an Adaptive Training in Informed Virtual Environment
Lo¨c Fricoteaux, Indira Thouvenin, J´ rˆ me Olive and Paul George ı eo
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.
1 Introduction
Virtual reality can provide, in comparison with classical training, many advantages [1]. In the case of fluvial navigation, training in virtual environment allows to simply modify environmental conditions (wind, current, etc.), which has an impact on the behavior of the ship. Another advantage of training in virtual reality is the strong coupling between the user and the virtual environment. The virtual world must credibly answers to user’s actions. We use an informed virtual environment (IVE: environment including knowledge-based models and providing an action/perception coupling) for fluvial navigation training. The purpose of our work is to provide the best learner guiding (set of aids) in real time based on learner observation. We propose an adaptive system: the learner’s behavior is taken into account for the choice of the aids to display [3]. On the opposite side, non-adaptive systems [4] are easier to build but the aids will not be adapted to the learner’s performance. For
Lo¨c Fricoteaux, Indira Thouvenin, J´ rˆ me Olive and Paul George ı eo
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.
1 Introduction
Virtual reality can provide, in comparison with classical training, many advantages [1]. In the case of fluvial navigation, training in virtual environment allows to simply modify environmental conditions (wind, current, etc.), which has an impact on the behavior of the ship. Another advantage of training in virtual reality is the strong coupling between the user and the virtual environment. The virtual world must credibly answers to user’s actions. We use an informed virtual environment (IVE: environment including knowledge-based models and providing an action/perception coupling) for fluvial navigation training. The purpose of our work is to provide the best learner guiding (set of aids) in real time based on learner observation. We propose an adaptive system: the learner’s behavior is taken into account for the choice of the aids to display [3]. On the opposite side, non-adaptive systems [4] are easier to build but the aids will not be adapted to the learner’s performance. For
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