Towards an intelligent tactical edge: an internet of battlefield things roadmap (Conference Presentation)

T. Abdelzaher, S. Russell
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Abstract

The paper presents a research agenda on supporting machine intelligence at the tactical network edge, and overviews early results in that space developed under the Internet of Battlefield Things Collaborative Research Alliance (IoBT CRA); a collaboration between the US Army Research Labs and a consortium of academia and industry led by the University of Illinois. It is becoming evident today that the use of artificial intelligence and machine learning components in future military operations will be inevitable. Yet, at present, the dependability limitations and failure modes of these components in a complex multi-domain battle environment are poorly understood. Most civilian research investigates solutions that exceed the SWaP (Size, Weight, and Power) limitations of tactical edge devices, and/or require communication with a central back-end. Resilience to adversarial inputs is not well developed. The need for significant labeling to train the machine slows down agility and adaptation. Cooperation between resource-limited devices to attain reliable intelligent functions is not a central theme. These gaps are filled by recent research emerging from the IoBT CRA. The paper reviews the field and presents the most interesting early accomplishments of the Alliance aiming to bridge the aforementioned capability gaps for future military operations.
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走向智能战术优势:战场物联网路线图(会议报告)
本文提出了在战术网络边缘支持机器智能的研究议程,并概述了在战场物联网协同研究联盟(IoBT CRA)下开发的该领域的早期成果;这是美国陆军研究实验室和由伊利诺伊大学领导的学术界和工业界联盟的合作项目。今天越来越明显的是,在未来的军事行动中使用人工智能和机器学习组件将是不可避免的。然而,目前对这些部件在复杂多域作战环境中的可靠性限制和失效模式了解甚少。大多数民用研究调查的解决方案超过战术边缘设备的SWaP(尺寸,重量和功率)限制,和/或需要与中央后端通信。对敌对投入的适应能力还没有得到很好的发展。需要大量的标签来训练机器,这减慢了机器的灵活性和适应性。在资源有限的设备之间进行合作以获得可靠的智能功能并不是一个中心主题。这些空白被IoBT CRA最近的研究所填补。本文回顾了该领域,并介绍了该联盟最有趣的早期成就,旨在弥合上述未来军事行动的能力差距。
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