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 - Publisher Copyright:
© 2020 IEEE.
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 - https://www.scopus.com/pages/publications/85100696286
UR - https://www.scopus.com/pages/publications/85100696286#tab=citedBy
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
T2 - 2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020
Y2 - 1 December 2020 through 3 December 2020
ER -