TY - GEN
T1 - Human-computer collaboration in adaptive supervisory control and function allocation of autonomous system teams
AU - Gutzwiller, Robert S.
AU - Lange, Douglas S.
AU - Reeder, John
AU - Morris, Rob L.
AU - Rodas, Olinda
N1 - Funding Information:
This work was supported by the Space and Naval Warfare Systems Center Pacific Naval Innovative Science and Engineering Program. This work was also supported by the US Department of Defense Autonomy Research Pilot Initiative under the project entitled “Realizing Autonomy via Intelligent Adaptive Hybrid Control”.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - The foundation for a collaborative, man-machine system for adaptive performance of tasks in a multiple, heterogeneous unmanned system teaming environment is discussed. An autonomics system is proposed to monitor missions and overall system attributes, including those of the operator, autonomy, states of the world, and the mission. These variables are compared within a model of the global system, and strategies that re-allocate tasks can be executed based on a mission-health perspective (such as relieving an overloaded user by taking over incoming tasks). Operators still have control over the allocation via a task manager, which also provides a function allocation interface, and accomplishes an initial attempt at transparency. We plan to learn about configurations of function allocation from human-in-the-loop experiments, using machine learning and operator feedback. Integrating autonomics, machine learning, and operator feedback is expected to improve collaboration, transparency, and human-machine performance.
AB - The foundation for a collaborative, man-machine system for adaptive performance of tasks in a multiple, heterogeneous unmanned system teaming environment is discussed. An autonomics system is proposed to monitor missions and overall system attributes, including those of the operator, autonomy, states of the world, and the mission. These variables are compared within a model of the global system, and strategies that re-allocate tasks can be executed based on a mission-health perspective (such as relieving an overloaded user by taking over incoming tasks). Operators still have control over the allocation via a task manager, which also provides a function allocation interface, and accomplishes an initial attempt at transparency. We plan to learn about configurations of function allocation from human-in-the-loop experiments, using machine learning and operator feedback. Integrating autonomics, machine learning, and operator feedback is expected to improve collaboration, transparency, and human-machine performance.
KW - Autonomics
KW - Autonomous systems
KW - Supervisory control
KW - Task models
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U2 - 10.1007/978-3-319-21067-4_46
DO - 10.1007/978-3-319-21067-4_46
M3 - Conference contribution
AN - SCOPUS:84947223062
SN - 9783319210667
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 447
EP - 456
BT - Virtual, Augmented and Mixed Reality - 7th International Conference, VAMR 2015 Held as Part of HCI International 2015, Proceedings
A2 - Shumaker, Randall
A2 - Lackey, Stephanie
PB - Springer Verlag
T2 - 7th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2015 Held as Part of 17th International Conference on Human-Computer Interaction, HCI International 2015
Y2 - 2 August 2015 through 7 August 2015
ER -