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Title: Designing Human-Autonomy Teaming Experiments Through Reinforcement Learning
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.  more » « less
Award ID(s):
1829008
PAR ID:
10284456
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Volume:
64
Issue:
1
ISSN:
2169-5067
Page Range / eLocation ID:
1426 to 1430
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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