Attachment to Technical Areas

Attachment to FA8750-23-S-7006 Technical Areas as of 8 SEP 2026 The Air Force Research Laboratory is soliciting white papers under this Broad Agency Announcement for research, development, integration, test and evaluation of technologies/techniques to support research in the following focus areas: Technical Area 1. Command & Control of AI Systems to Achieve Mission Tailored AI

OPERATIONAL CONTEXT FOR PROJECT “BATTLE MANAGEMENT OF AI”

The Air Force Research Laboratory is soliciting white papers under Technical Area 1 for research, development, integration, test, and evaluation of technologies/techniques that augment battle management systems with novel control processes for adapting behavior of artificial intelligence (AI) capabilities during mission execution. The resultant “Battle Management of AI” will enable operators to reason through a complex space of competing design factors and mission dependencies to select and deploy AI components that are compatible with mission tactics and evolving battlefield conditions. This capability will provide AF tactical operations with an improved level of agility and responsiveness that is paramount for safe and effective utilization of AI-based warfighting systems.
Modern warfare has become increasingly dependent on AI-based warfighting systems that use trained models to perform tasks at speed and scales beyond human capacity. These AI-based systems can support a variety of functions such as classification of targets for ISR and control of autonomous vehicles for combat. Because models are trained a priori on data (and simulations) in an anticipatory fashion, AI-based systems encounter situations in the real world that are incompatible with training feature distributions and parameterization of employed algorithms. The result is degradation to model performance that can negatively impact mission effectiveness and safety. Therefore, the Air Force requires new battle management processes to monitor performance of AI-based systems and update incumbent models in response to changing battlespace conditions. In the trivial case, operators will simply repurpose a pretrained model that fortuitously fulfills unanticipated mission requirements. In the extreme case, operators will coordinate a distributed workflow, known as an AI COA, to retrain, test, and deploy new models in line with mission execution, so that dependent systems can continue to function as intended with minimal loss of service. This process to detect shifts in performance of AI-based systems