Designing Human-Autonomy Teaming Experiments Through Reinforcement Learning. (December 2020)

• Record Type: Journal Article
• Authors: Schelble, Beau; Canonico, Lorenzo-Barberis; McNeese, Nathan; Carroll, Jack; Hird, Casey
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 64:Part 1(2020)
• Page Start: 1426
• Page End: 1430
• Publication Date: 2020-12
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1071181320641340
• Languages: English
• ISSNs: 1541-9312

Abstract

This paper creates and defines a framework for building and implementing human-autonomy teaming experiments that enable the utilization of modern reinforcement learning models. These models are used to train artificial agents to then interact alongside humans in a human-autonomy team. The framework was synthesized from experience gained redesigning a previously known and validated team task simulation environment known as NeoCITIES. Through this redesign, several important high-level distinctions were made that regarded both the artificial agent and the task simulation itself. The distinctions within the framework include gamification, access to high-performance computing, a proper reward function, an appropriate team task simulation, and customizability. This framework enables researchers to create experiments that are more usable for the human and more closely resemble real-world human-autonomy interactions. The framework also allows researchers to create veritable and robust experimental platforms meant to study human-autonomy teaming for years to come.