TY - GEN
T1 - The Complex Dynamics of Team Situation Awareness in Human-Autonomy Teaming
AU - Grimm, David
AU - Demir, Mustafa
AU - Gorman, Jamie C.
AU - Cooke, Nancy
N1 - Funding Information:
This research is supported by ONR Award N000141712382 (Program Managers: Marc Steinberg, Micah Clark). The authors acknowledge Steven M. Shope from Sandia Research Corporation who integrated the synthetic teammate into the synthetic environment, and Nathan J. McNeese who contributed on the experimental design.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/31
Y1 - 2018/7/31
N2 - This study explores how Human-Autonomy Teams work together in a Remotely Piloted Aircraft System (RPAS) to overcome three types of degraded conditions, including automation and autonomy failures, and malicious attack. The two human participants were informed that the pilot was a "synthetic" agent that has limited communication capacity. For in-depth exploratory analysis, we identified one high- and one low-performing team in terms of overcoming failures and malicious attack, and then we used nonlinear dynamical methods to understand how human-autonomy interactions might affect overall Team Situation Awareness (TSA) in terms of level of complexity. We first produced Joint Recurrence Plots (JRP) to demonstrate predictability of team communication behavior during the TSA. After that, in order to identify how flexible the team was during degraded conditions, we examined entropy across four layers to represent RPAS:communication - chat-based interactions; vehicle - the RPA itself; control - user interface; and system - total activity of all layers. Results from the JRP showed that the high performing team communicated more effectively than the low performing team during the all three types of failures, while the entropy analysis showed that the high performing team appeared to be more flexible in their communication and overall system patterns.
AB - This study explores how Human-Autonomy Teams work together in a Remotely Piloted Aircraft System (RPAS) to overcome three types of degraded conditions, including automation and autonomy failures, and malicious attack. The two human participants were informed that the pilot was a "synthetic" agent that has limited communication capacity. For in-depth exploratory analysis, we identified one high- and one low-performing team in terms of overcoming failures and malicious attack, and then we used nonlinear dynamical methods to understand how human-autonomy interactions might affect overall Team Situation Awareness (TSA) in terms of level of complexity. We first produced Joint Recurrence Plots (JRP) to demonstrate predictability of team communication behavior during the TSA. After that, in order to identify how flexible the team was during degraded conditions, we examined entropy across four layers to represent RPAS:communication - chat-based interactions; vehicle - the RPA itself; control - user interface; and system - total activity of all layers. Results from the JRP showed that the high performing team communicated more effectively than the low performing team during the all three types of failures, while the entropy analysis showed that the high performing team appeared to be more flexible in their communication and overall system patterns.
KW - Human-Autonomy Teaming
KW - Malicious Attacks
KW - Nonlinear Dynamical Systems
KW - Team Situation Awareness
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U2 - 10.1109/COGSIMA.2018.8423990
DO - 10.1109/COGSIMA.2018.8423990
M3 - Conference contribution
AN - SCOPUS:85051462394
SN - 9781538652886
T3 - Proceedings - 2018 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2018
SP - 103
EP - 109
BT - Proceedings - 2018 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2018
A2 - Gundersen, Odd Erik
A2 - Lebiere, Christian
A2 - Rogova, Galina L.
A2 - Baclawski, Ken
A2 - Salfinger, Andrea
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2018
Y2 - 11 June 2018 through 14 June 2018
ER -