Comparing Learning Techniques for Hidden Markov Models of Human Supervisory Control Behavior

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.