大规模智能数据融合系统的不确定性与鲁棒性

Benjamin M. Marlin, T. Abdelzaher, G. Ciocarlie, Adam D. Cobb, Mark S. Dennison, Brian Jalaian, Lance M. Kaplan, Tiffany R. Raber, A. Raglin, P. Sharma, M. Srivastava, T. Trout, Meet P. Vadera, Maggie B. Wigness
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引用次数: 5

摘要

近十年来,人工智能的兴起极大地改变了现代传感器数据融合系统的设计,带来了新的挑战、机遇和研究方向。其中一个挑战是对不确定性的管理。本文开发了一个框架来推理不确定性的来源,发展了不确定性的表示,并研究了现代智能数据处理系统中的不确定性缓解策略。洞察被开发成工作流组合,在利用人类、算法和机器学习组件的协作的同时,最大限度地提高完成任务目标的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Uncertainty and Robustness in Large-Scale Intelligent Data Fusion Systems
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.
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