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A Hybrid Architecture for Non-technical Skills Diagnosis

  • Yannick BourrierEmail author
  • Francis Jambon
  • Catherine Garbay
  • Vanda Luengo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10858)

Abstract

Our Virtual Learning Environment aims at improving the abilities of experienced technicians to handle critical situations through appropriate use of non-technical skills (NTS), a high-stake matter in many domains as bad mobilization of these skills is the cause of many accidents. To do so, our environment dynamically generates critical situations designed to target these NTS. As the situations need to be adapted to the learner’s skill level, we designed a hybrid architecture able to diagnose NTS. This architecture combines symbolic knowledge about situations, a neural network to drive the learner’s performance evaluation process, and a Bayesian network to model the causality links between situation knowledge and performance to reach NTS diagnosis. A proof of concept is presented in a driving critical situation.

Keywords

Neural networks Bayesian networks Ill-defined domains 

Notes

Funding

This research is funded by the French National Research Agency (ANR) via the MacCoyCritical Project (ANR-14-CE24-0021).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yannick Bourrier
    • 1
    • 2
    Email author
  • Francis Jambon
    • 2
  • Catherine Garbay
    • 2
  • Vanda Luengo
    • 1
  1. 1.Sorbonne Université, CNRS, LIP6ParisFrance
  2. 2.Univ. Grenoble Alpes, CNRS, Grenoble INP, LIGGrenobleFrance

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