Date of Award


Document Type


Degree Name

Master of Science


Department of Systems Engineering and Management

First Advisor

Michael E. Miller, PhD.


As demand for the number of Unmanned Aerial Vehicle (UAV) sorties increases faster than the number of available operators, a significant Air Force research thrust includes the vision of a single operator supervising multiple UAVs; this involves increasing use of automation, creating the potential for the operators to become complacent and over-reliant on automation. To avoid operator complacency, adaptive automation has been proposed, where changes in automation are triggered based upon operator performance or other attributes. This research sought to understand the effect of a weighted method for triggering changes in automation within a multitasking environment as compared to a more traditional method in which performance on tasks is treated equally. In this work, the weighted method considered the priority of each task when computing a measure of operator performance on which to trigger changes in automation. Although overall system, consisting of both the operator and automation system, performance was not statistically different between the two trigger implementations, the participants with the priority based triggering scheme tended to rate the level of automation changes as more aligned with their actual performance and were significantly less surprised by the actions of the automation than those participants with the non-weighted approach. The results of this study, combined with participant preference for workload based adaptations, suggest a benefit to the implementation of a hybrid approach. Future research should focus on task weights based on priority and operator specific threshold criteria, where automation aides are triggered once the summation of current tasks exceeds the given threshold.

AFIT Designator


DTIC Accession Number