Stochastic approximation to optimize the performance of human operators

Abstract

Motivated by optimizing the performance of human operators of Unmanned Aircraft Systems (UAS), we consider the use of stochastic approximation algorithms in this paper. With the increasing levels of automation available for both military and civilian unmanned vehicle systems, the human operators are expected to contribute as high-level planners and decision makers more than as remote-control pilots. Humans and, to a lesser extent, unmanned vehicles, are limited by workload. To improve the performance of the mixed systems of humans and unmanned vehicles, it is important to find the workload for human operators that will achieve the best rate of correct decision making. Although the performance of human operators is known to be a concave function of their arousal level, as described by the Yerkes-Dodson law, precise descriptions of such a function and how workload related to arousal level remain unknown in general. Furthermore, assessing the correctness of decisions is difficult in practice due to uncertainties in real situations, and due to the small number of data sets available for training of operators, and cost of such training. To bypass these difficulties and optimize operators' performance, we adjusted traditional stochastic approximation formulation and developed algorithm to solve it. Our approach can be used to optimize the performance of multiple human operators without knowing the correctness of any individual's decisions.