Abstract
This paper describes the importance of visualization to support after-action review technologies to develop appropriate human-autonomy team dynamics. Key to developing accurate visualizations in this context requires the capability to quantify critical team dynamics by parsing large, diverse sensor data in offline, low-latency environments. Here, we describe the use of open-source technology and scalable web-based applications for the quick ingestion of these massive datasets and timely visualizations for the AAR process for the development of the Global After-Action Review Technology. This technology explores human-autonomy team interaction problems by identifying key features needed for computer vision, including image classification, object recognition, scene understanding, gait recognition, pose estimation, tracking, and behavior recognition.
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References
Smith-Jentsch, K.A., Johnston, J.H., Payne, S.C.: Measuring team-related expertise in complex environments (1998)
McNeese, N.J., Demir, M., Cooke, N.J., Myers, C.: Teaming with a synthetic teammate: insights into human-autonomy teaming. Hum. Factors 60, 262–273 (2018)
De Dreu, C.K., Weingart, L.R.: Task versus relationship conflict, team performance, and team member satisfaction: a meta-analysis. J. Appl. Psychol. 88, 741 (2003)
Marks, M.A., Mathieu, J.E., Zaccaro, S.J.: A temporally based framework and taxonomy of team processes. Acad. Manag. Rev. 26, 356–376 (2001)
Blickensderfer, E., Cannon-Bowers, J.A., Salas, E.: Theoretical bases for team self-correction: fostering shared mental models. Adv. Interdisc. Stud. Work Teams 4, 249–279 (1997)
Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors 37, 32–64 (1995)
Salas, E., Sims, D.E., Burke, C.S.: Is there a “big five” in teamwork? Small Group Res. 36, 555–599 (2005)
Marks, M.A., Panzer, F.J.: The influence of team monitoring on team processes and performance. Hum. Perform. 17, 25–41 (2004)
Schaefer, K.E., Aksaray, D., Wright, J., Roy, N.: Challenges with addressing the issue of context within AI and human-robot teaming. In: Computational Context, pp. 80–96. CRC Press (2018)
Schaefer, K.E., Oh, J., Aksaray, D., Barber, D.: Integrating context into artificial intelligence: research from the robotics collaborative technology alliance. AI Mag. 40, 28–40 (2019)
Brewer, R.W., Cerame, E., Pursel, E.R., Zimmermann, A., Schaefer, K.E.: Manned-unmanned teaming: US army robotic wingman vehicles. In: Cassenti, D.N. (ed.) AHFE 2018. AISC, vol. 780, pp. 89–100. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94223-0_9
Schaefer, K.E., Baker, A.L., Brewer, R.W., Patton, D., Canady, J., Metcalfe, J.S.: Assessing multi-agent human-autonomy teams: US Army Robotic Wingman gunnery operations. In: Micro-and Nanotechnology Sensors, Systems, and Applications XI, p. 109822B. International Society for Optics and Photonics (2019)
Brewer, R.W., Walker, A.J., Pursel, E.R., Cerame, E.J., Baker, A.L., Schaefer, K.E.: Assessment of manned-unmanned team performance: comprehensive after-action review technology development. In: Chen, J. (ed.) AHFE 2019. AISC, vol. 962, pp. 119–130. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-20467-9_11
Uttal, D.H., O’Doherty, K.: Comprehending and learning from ‘visualizations’: a developmental perspective. In: Gilbert, J.K., Reiner, M., Nakhleh, M. (eds.) Visualization: Theory and Practice in Science Education, pp. 53–72. Springer, Dordrecht (2008). https://doi.org/10.1007/978-1-4020-5267-5_3
Rapp, D.N., Kurby, C.A.: The ‘ins’ and ‘outs’ of learning: Internal representations and external visualizations. In: Gilbert, J.K., Reiner, M., Nakhleh, M. (eds.) Visualization: Theory and Practice in Science Education, pp. 29–52. Springer, Dordrecht (2008). https://doi.org/10.1007/978-1-4020-5267-5_2
Aigner, W., Miksch, S., Müller, W., Schumann, H., Tominski, C.: Visualizing time-oriented data—a systematic view. Comput. Graph. 31, 401–409 (2007)
Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (2001)
Von Krogh, G., Von Hippel, E.: The promise of research on open source software. Manag. Sci. 52, 975–983 (2006)
Command, U.: Training and qualification, crew. US), DotA (ed.) Training Circular No.: TC 3-20.31 (2015)
Baker, A.L., Schaefer, K.E., Hill, S.G.: Teamwork and Communication Methods and Metrics for Human-Autonomy Teaming. CCDC Army Research Laboratory Aberdeen Proving Ground United States (2019)
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Taberski, M., Davis, K., Schaefer, K.E., Brewer, R. (2021). Visualizing Human-Autonomy Team Dynamics Through the Development of a Global After-Action Review Technology. In: Wright, J.L., Barber, D., Scataglini, S., Rajulu, S.L. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2021. Lecture Notes in Networks and Systems, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-030-79763-8_6
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