Supervised vs unsupervised learning for operator state modeling in unmanned vehicle settings


Journal Article

In this paper, we model operator states using hidden Markov models applied to human supervisory control behaviors. More specifically, we model the behavior of an operator of multiple heterogeneous unmanned vehicle systems. The hidden Markov model framework allows the inference of higher operator states from observable operator interaction with a computer interface. For example, a sequence of operator actions can be used to compute a probability distribution of possible operator states. Such models are capable of detecting deviations fromexpected operator behavior as learned by the model.The difficulty with parametric inference models such as hidden Markov models is that a large number of parameters must either be specified by hand or learned from example data.We compare the behavioral models obtained with two different supervised learning techniques and an unsupervised hidden Markov model training technique. The results suggest that the best models of human supervisory control behavior are obtained through unsupervised learning. We conclude by presenting further extensions to this work. © 2011 by the Yves Boussemart, Mary L. Cummings, Jonathan Las Fargeas and Nicholas Roy.

Full Text

Duke Authors

Cited Authors

  • Boussemart, Y; Cummings, ML; Las Fargeas, J; Roy, N

Published Date

  • March 1, 2011

Published In

Volume / Issue

  • 8 / 3

Start / End Page

  • 71 - 85

Electronic International Standard Serial Number (EISSN)

  • 1542-9423

International Standard Serial Number (ISSN)

  • 1542-9423

Digital Object Identifier (DOI)

  • 10.2514/1.46767

Citation Source

  • Scopus