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

Addison W. Bohannon; Sean M. Fitzhugh; Arwen H. DeCostanza

doi:10.1117/12.2519066

10 May 2019 A framework for enhancing human-agent teamwork through adaptive individualized technologies

Addison W. Bohannon, Sean M. Fitzhugh, Arwen H. DeCostanza

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110060Y (2019) https://doi.org/10.1117/12.2519066
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Addison W. Bohannon, Sean M. Fitzhugh, and Arwen H. DeCostanza "A framework for enhancing human-agent teamwork through adaptive individualized technologies", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060Y (10 May 2019); https://doi.org/10.1117/12.2519066

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available