This paper presents a methodology for determining whether human operators anticipate future control needs in order to compensate for time delays when controlling remote vehicles. The approach utilizes techniques drawn from the machine learning community in order to learn statistical models of human decision making. Models are fit to an experimental data set generated by remote operations of a robot subjected to time delays between 0 and 2.5 s, using the least angle regression (LARS) and sparse multinomial logistic regression (SMLR) algorithms. These algorithms make use of regularization to reduce the effects of overparameterization due to redundant or noisy environmental features. Models learned by LARS achieve an average prediction rate between 81% and 98%, depending on time delay, while those learned by SMLR achieve average rates between 68% and 86%. A novel metric of feature “importance” is used to evaluate the relative contributions of environmental features to model performance, motivated by the structure of the LARS algorithm. The degree to which human operators rely on anticipation is determined by examining how “importance” scores for features representing different prediction horizons vary with increasing time delay.
['Mark McClelland', 'Mark E. Campbell']
Probabilistic Modeling of Anticipation in Human Controllers