Investigation of Critical Attributes for Transparency and Operator Performance in Human Autonomy Teaming (TOPHAT) for Intelligent Mission Planning

Teams tend to be high-performing when they have an accurate shared mental model. A shared mental model (SMM) is the understanding of the exterior world, as well as who within a team has both the ability to perform certain tasks as well as the responsibility to see that they are performed correctly. It incorporates understanding bout who has access to what information and what communication mechanisms are in place. It also incorporates the prior experiences of the team that allows team members to reference and leverage those experiences to reduce communication burdens.  

While significant research has been conducted on SMMs developed between human-centric teams, less is understood about the importance of, and the mechanisms necessary to create and maintain a shared mental model between humans and more sophisticated automation, i.e. autonomy and particularly autonomy found in learning agents such as those powered by AI or Machine Learning. We wish to leverage the creative and adaptable capabilities of humans and the horsepower of machines to provide maximal task and team performance using human-autonomy teaming. In such cases, the SMM must exist in both the human mind and in the agent’s memory structures.  It must be updatable, and changes must be communicated in both directions. And it must be used by the autonomous agent to reason and make decisions.  

This research focuses primarily on understanding the kinds of mechanisms by which a shared mental model could be created, and changes passed to a human from an autonomous agent. We investigate the critical attributes that impact the formation and maintenance of a SMM between a human and an AI teammate to better understand how shared mental models can improve human-autonomy teaming by facilitating collaborative judgment and shared situational awareness.  

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