Comparing Learning Techniques for Hidden Markov Models of Human Supervisory Control Behavior
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Cummings_ComparingLearning.pdf
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Author(s)
Cummings, M. L.
Las Fargeas, Jonathan C.
Roy, Nicholas
Boussemart, Yves
Date Issued
April 2009
Journal
AIAA Infotech@Aerospace'09 Conference, Seattle, Washington
Publisher
American Institute of Aeronautics and Astronautics
Citation
Boussemart, Yves et al. "Comparing Learning Techniques for Hidden Markov Models of Human Supervisory Control Behavior." AIAA Infotech@Aerospace'09 Conference and AIAA Unmanned...Unlimited Conference, 6-9 April 2009, Seattle, Washington.
Version
Original manuscript
Abstract
Models of human behaviors have been built using many different frameworks. In this paper, we make use of Hidden Markov Models (HMMs) applied to human supervisory control behaviors. More specifically, we model the behavior of an operator of multiple heterogeneous unmanned vehicle systems. The HMM framework allows the inference of higher operator cognitive 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 from expected operator behavior as learned by the model. The difficulty with parametric inference models such as HMMs 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 HMM training technique. The results suggest that the best models of human supervisory control behavior are obtained through unsupervised learning.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Humans and Automation Lab
Massachusetts Institute of Technology. School of Engineering
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DOI of Published Version
http://www.aiaa.org/agenda.cfm?lumeetingid=2070&viewcon=agenda&pageview=2&programSeeview=1&dateget=07-Apr-09&formatview=3
