On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems

被引：4

作者：

Marlin, Benjamin M. ^{[1
]}

Abdelzaher, Tarek ^{[2
]}

Ciocarlie, Gabriela ^{[3
]}

Cobb, Adam D. ^{[4
]}

Dennison, Mark ^{[4
]}

Jalaian, Brian ^{[4
]}

Kaplan, Lance ^{[4
]}

Raber, Tiffany ^{[4
]}

Raglin, Adrienne ^{[4
]}

Sharma, Piyush K. ^{[4
]}

Srivastava, Mani ^{[5
]}

Trout, Theron ^{[6
]}

Vadera, Meet P. ^{[1
]}

Wigness, Maggie ^{[4
]}

机构：

[1] Univ Massachusetts, Amherst, MA 01003 USA

[2] Univ Illinois, Champaign, IL USA

[3] SRI Int, Menlo Pk, CA USA

[4] US Army Res Lab, Adelphi, MD USA

[5] Univ Calif Los Angeles, Los Angeles, CA USA

[6] Stormfish Sci Corp, Silver Spring, MD USA

来源：

2020 IEEE SECOND INTERNATIONAL CONFERENCE ON COGNITIVE MACHINE INTELLIGENCE (COGMI 2020) | 2020年

关键词：

Uncertainty Analysis; Cyber-physical Systems; Machine Intelligence; PROBABILITY;

D O I：

10.1109/CogMI50398.2020.00020

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：82 / 91

页数：10

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