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.
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Zhang, Z., Vanderhaegen, F., Millot, P. (2006). Prediction of Human Behaviour Using Artificial Neural Networks. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_80
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DOI: https://doi.org/10.1007/11739685_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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