Informed interaction by a decision-making system for a multimodal feedback in virtual environements : application to fluvial navigation training
type: Dataset
description: This thesis deals with interaction between human and informed virtual environment (IVE). An IVE is a virtual environment including knowledge, which is, in our case, linked with virtual objects (Smart Objects). The purpose of this thesis is to enhance the coupling between human and IVE by allowing it to relevantly react to user’s behavior. To do so, we add to the IVE a decision-making module which is able to choose, in real time, a multimodal feedback (a set of aids) according to user’s activity which is observed by the system by using various sensors. We choose the belief function theory for decision-making in order to deal with the uncertainty and the incompleteness of collected data describing user’s behavior. In this way, our system brings an informed interaction: the reaction of the system to user’s actions is based on a priori knowledge combined with other knowledge acquired in real time. Thus, we bring a personalized interaction, adapted to each user, to increase its efficiency. The decision-making module includes an evidential network with conditional belief functions allowing graphically representing influences, a priori known, between different variables of the system. Input variables correspond to data collected in real time about the user: his/her actions and physiological state. These data can be uncertain (ex: data coming from sensors with some reliability) or incomplete (ex: failure of one of the sensors). The belief function theory allows us to represent these data with the uncertainty and the ignorance which are associated. These beliefs are then propagated in the evidential network to obtain, in output, a belief on the utility of the triggering for each available multimodal aid. The choice concerning the display of a set of multimodal aids, among those having an important utility, is done by solving a constraint satisfaction problem. Indeed, the set of multimodal aids to choose must respect a set of constraints which are a priori or dynamically set …
issued: 2026-05-06T22:19:37.050381
language: fr
modified: 2026-05-06T22:19:37.050387
contact point: Organization
  fn: CCSD
keyword: belief-function-theory
keyword: constraint-satisfaction
keyword: evidential-network
keyword: incertitude
keyword: infoeu-reposemanticsreport
keyword: infoinfo-aicomputer-science-csartificial-intelligence-csai
keyword: infoinfo-grcomputer-science-csgraphics-csgr
keyword: informed-interaction
keyword: interaction-informee
keyword: multimodal-feedback
keyword: observation-de-lutilisateur
keyword: reports
keyword: reseau-evidentiel
keyword: retour-multimodal
keyword: satisfaction-de-contraintes
keyword: theorie-des-fonctions-de-croyance
keyword: uncertainty
keyword: user-observation
landing page: https://hal.science/hal-00944648

Informed interaction by a decision-making system for a multimodal feedback in virtual environements : application to fluvial navigation training
type: Distribution
format: HTML
issued: 2026-05-06T22:19:37.133455
modified: 2026-05-06T22:19:37.023683
access URL: https://hal.science/hal-00944648

test_moissonnage_selune
type: Agent