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Proceedings Paper

A framework for enhancing human-agent teamwork through adaptive individualized technologies
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Paper Abstract

Future military operations will require teams of Soldiers and intelligent systems to plan and execute collective action in a dynamic and adversarial environment. In human teams, teamwork processes such as effective communication and shared understanding underlie effective team performance. Recent work proposes a vision for generalizing this theory to human-agent teams and facilitating teamwork via individualized, adaptive technologies. We propose a dynamical system model to understand how individualized, adaptive technology can facilitate teamwork in human-agent teams. The model reveals three scientific challenges: describing the dynamics of team state, understanding how technological interventions will manifest in team states, and observing latent teamwork states. Using this model, we motivate a problem in which we predict team outcomes from non-obtrusive observation of a military staff during a training exercise. Representing pairwise interactions between team members as a weighted adjacency matrix, we use low-rank matrix recovery techniques to identify communication patterns that predict external evaluations of three team processes during task completion: effective communication, shared understanding, and positive affect.

Paper Details

Date Published: 10 May 2019
PDF: 8 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060Y (10 May 2019); doi: 10.1117/12.2519066
Show Author Affiliations
Addison W. Bohannon, U.S. Army Research Lab. (United States)
Sean M. Fitzhugh, U.S. Army Research Lab. (United States)
Arwen H. DeCostanza, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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