The Effect of Similarity between Human and Machine Action Choices on Adaptive Automation Performance

Abstract

One of the defining characteristics of an adaptive automation system is the hand-off from machine to human--and vice versa. This research seeks to improve system control hand-offs, by investigating how the manner in which the automation completes its task affects the overall performance of the human-machine team. Specifically, the research will explore how the level of similarity of action choices between the automation and the human operator affects the resulting system's performance. A design process model for creating adaptive automation systems is complete, and was used to design an adaptive automation research environment. Data gathered using this system will be used to automate user task performance in the system, and allow for research into the effects of that automation.