A Machine Learning Approach to the Determination of Value of Information to Operators and Applications on the Tactical Edge

Perry, Jacques M.; Galliera, Raffaele; Suri, Niranjan

doi:10.1016/j.procs.2022.09.015

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…6 Perry et al described a tree-search approach to compute Value of Information (VoI) of individual personnel in a tactical environment, which determines whether a given piece of information is relevant to an operator. 7 This calculation resulted in an efficient mechanism to minimize excess network traffic in austere environments. Barz et al explored the characteristics of adaptive communications at the tactical edge and focused partly on "joint orchestration of network, processing, and AI" to improve decisionmaking and other actions critical to this domain.…”

Section: Networkmentioning

confidence: 99%

Tactical agility for AI-enabled multi-domain operations

Sturzinger¹,

George²,

Satyanarayanan³

2023

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V

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Commanders must remain agile and adaptive in the future Artificial Intelligence (AI)-enabled multi-domain battlespace, where critical decisions are made at the tactical edge. Over-reliance on static, cloud-centric approaches to Machine Learning Operations (MLOps) compromises such agility and adaptability. These systems must operate in a dynamic threat environment, and learn to detect novel threats during operation. They must be able to perform this learning through the execution of tactical MLOps under austere and degraded conditions, especially limited wireless network bandwidth. In response to these requirements, this paper describes Hawk, a system that leverages edge proximity for rapid and iterative execution of the Observe stage of the Observe-Orient-Decide-Act loop. Central to this architecture is the use of tactical cloudlets. These mini data centers provide cloud-like computing resources without the communication latency to exascale data centers. Hawk enables a human to guide MLOps at low cognitive load, thus enabling an operational objective to be achieved at speed and scale while remaining usable and explainable.

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Section: Networkmentioning

confidence: 99%