TY - GEN
T1 - On uncertainty and robustness in large-scale intelligent data fusion systems
AU - Marlin, Benjamin M.
AU - Abdelzaher, Tarek
AU - Ciocarlie, Gabriela
AU - Cobb, Adam D.
AU - Dennison, Mark
AU - Jalaian, Brian
AU - Kaplan, Lance
AU - Raber, Tiffany
AU - Raglin, Adrienne
AU - Sharma, Piyush K.
AU - Srivastava, Mani
AU - Trout, Theron
AU - Vadera, Meet P.
AU - Wigness, Maggie
N1 - Funding Information:
Research reported in this paper was sponsored in part by the Army Research Laboratory under Cooperative Agreement W911NF-17-20196, DARPA award W911NF-17-C- 0099, and DTRA award HDTRA118-1-0026. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies of the CCDC Army Research Laboratory, DARPA, DTRA, or the US government. The US government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation hereon.
Funding Information:
ACKNOWLEDGMENT Research reported in this paper was sponsored in part by the Army Research Laboratory under Cooperative Agreement W911NF-17-20196, DARPA award W911NF-17-C-0099, and DTRA award HDTRA118-1-0026. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies of the CCDC Army Research Laboratory, DARPA, DTRA, or the US government. The US government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation hereon.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - The resurgence of AI in the recent decade dramatically changes the design of modern sensor data fusion systems, leading to new challenges, opportunities, and research directions. One of these challenges is the management of uncertainty. This paper develops a framework to reason about sources of uncertainty, develops representations of uncertainty, and investigates uncertainty mitigation strategies in modern intelligent data processing systems. Insights are developed into workflow composition that maximizes efficacy at accomplishing mission goals despite the sources of uncertainty, while leveraging a collaboration of humans, algorithms, and machine learning components.
AB - The resurgence of AI in the recent decade dramatically changes the design of modern sensor data fusion systems, leading to new challenges, opportunities, and research directions. One of these challenges is the management of uncertainty. This paper develops a framework to reason about sources of uncertainty, develops representations of uncertainty, and investigates uncertainty mitigation strategies in modern intelligent data processing systems. Insights are developed into workflow composition that maximizes efficacy at accomplishing mission goals despite the sources of uncertainty, while leveraging a collaboration of humans, algorithms, and machine learning components.
KW - Cyber-physical Systems
KW - Machine Intelligence
KW - Uncertainty Analysis
UR - http://www.scopus.com/inward/record.url?scp=85100696286&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100696286&partnerID=8YFLogxK
U2 - 10.1109/CogMI50398.2020.00020
DO - 10.1109/CogMI50398.2020.00020
M3 - Conference contribution
AN - SCOPUS:85100696286
T3 - Proceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020
SP - 82
EP - 91
BT - Proceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020
Y2 - 1 December 2020 through 3 December 2020
ER -