Human-autonomy teaming using flexible human performance models: An initial pilot study

Abstract

Recent advances in autonomy have highlighted opportunities for tight coordination between humans and autonomous agents in many current and future applications. For operations involving cooperating humans and robots, autonomous teammates must have flexibility to respond to the inherently unpredictable behavior of their human counterparts. To investigate this issue in detail, this paper uses an unmanned aerial vehicle (UAV) simulation to evaluate flexible human performance models over traditional static modeling approaches for multi-agent task allocation and scheduling. Additional comparisons are drawn between adaptive human models, which are adjusted in real-time by the autonomous planner according to realized human performance, and adaptable human models, where the human operator is given sole authority over model adjustments. Results indicate that adaptive human performance models significantly increase total mission reward over both the baseline static modeling framework (p = 0.0012) as well as the adaptable modeling technique (p = 0.0028) for this system.