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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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