Prediction of human behaviour using artificial neural networks

  • Authors:

  • Zhicheng Zhang;Frédéric Vanderhaegen;Patrick Millot

  • Affiliations:

  • Division of I&C and Electrical Systems, Framatome ANP, Tour Areva, Paris La Defense, France;CNRS UMR 8530, Laboratoire d’Automatique, de Mécanique et d’Informatique industrielles et Humaines, University of Valenciennes, Le Mont Houy, Valenciennes, France;CNRS UMR 8530, Laboratoire d’Automatique, de Mécanique et d’Informatique industrielles et Humaines, University of Valenciennes, Le Mont Houy, Valenciennes, France

  • Venue:
  • ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
  • Year:
  • 2005

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Abstract

This paper contributes to the analysis and prediction of deviate intentional behaviour of human operators in Human-Machine Systems using Artificial Neural Networks that take uncertainty into account. Such deviate intentional behaviour is a particular violation, called Barrier Removal. The objective of the paper is to propose a predictive Benefit-Cost-Deficit model that allows a multi-reference, multi-factor and multi-criterion evaluation. Human operator evaluations can be uncertain. The uncertainty of their subjective judgements is therefore integrated into the prediction of the Barrier Removal. The proposed approach is validated on a railway application, and the prediction convergence of the uncertainty-integrating model is demonstrated.