Air Force asks industry for artificial intelligence (AI) and machine learning in command and control

March 5, 2024
Primary challenges involve managing AI for command, control communications, computers, and intelligence (C4I) in the presence of the enemy.

ROME, N.Y. – U.S. Air Force researchers are asking for industry's help in developing ways to apply artificial intelligence (AI) to distributed command and control in contested environments.

Officials of the Air Force Research Laboratory Information Directorate in Rome, N.Y., has released a broad agency announcement (FA8750-23-S-7006) for the Artificial Intelligence and Next Generation Distributed Command and Control project, which has eight technical areas.

Primary challenges involve the ability to manage AI for command, control communications, computers, and intelligence (C4I) in the presence of the enemy.

Technical areas are: command and control of AI to achieve mission-tailored AI; federated, composable autonomy and AI toolbox; advanced wargaming agents; interactive learning for C4I; command and control complexity dominance generative AI C4I; software defined distributed command and control; and tactical AI.

AI holds potential to enable military decision makers to assess the battlespace, select the best plan, and direct forces in a distributed setting.

Related: Wanted: artificial intelligence (AI) and machine autonomy algorithms for military command and control

The Air Force is trying to switch from monolithic command and control node to distributed command and control. To do this, Air Force leaders would like to apply AI to command and control, and to consider enemy AI use in mission planning.

Command and control of AI to achieve mission-tailored AI focuses on adapting AI models to specific problems quickly, and to define the roles, responsibilities, and supporting infrastructure. It seeks to develop battle management tools that bring together a distributed team of specialists to train and deploy mission-tailored AI.The technical contact is Nicholas Del Rio, whose email address is [email protected].

Federated, composable autonomy and AI toolbox seeks to develop collaborative federated AI tools to help share information raw data, and define interfaces and standards to enable third parties to develop AI and machine learning components. The technical contact is Patrick Fisher, whose email is [email protected].

Advanced wargaming agents seeks to incorporate AI and wargaming technology to wargames, evaluation benchmarks, and gaming environments. The technical contact is Capt. Shaun Ryer at [email protected].

Related: Air Force researchers ask industry for artificial intelligence (AI) and machine learning for military C4ISR

Interactive learning for C4I data involves efficient machine learning approaches that use humans to train a model quickly to make inferences for planning intelligence tipping and queuing, course of action generation, image or spectrum analysis, hyper-parameter tuning, and acceleration of high-fidelity modeling and simulation environments. The technical contact is Daniel Carpenter at [email protected].

Command and control complexity dominance generative AI C4I focuses on ways to exploit operational complexity to impose complexity on enemy human and AI agents to influence the adversary’s decisions and actions. The technical contact is Ashley Prater-Bennette at [email protected].

Software defined distributed command and controls seeks to explore generative AI in C4I through prototyping use-cases. The technical contact is Edward Verenich at [email protected].

Software-defined distributed command and control focuses on ways to optimize distributed command and control resources that are distributed across the theater in the presence of the enemy. The technical contact is Ryan Hilliard at [email protected].

Related: Wanted: artificial intelligence (AI) and machine learning to help humans and computers work together

Tactical AI focuses on ways to use distributed sensors to detect, reason over, and mitigate sources of interference by developing efficient command and control and intelligent central control. Scalability is a primary consideration. The technical contact is David Castello at [email protected].

The Artificial Intelligence and Next Generation Distributed Command and Control project should spend about $99 million over the next four years, and several contract awards are expected. The project will accept white papers until March 2027.

Companies interested should email white papers to each technical area's technical contact, and to the Air Force's Gennady Staskevich at [email protected]. Those submitting promising white papers may be asked to submit full proposals.

Email technical questions to Gennady Staskevich at [email protected], and business questions to Amber Buckley at [email protected]. More information is online at https://sam.gov/opp/d8eb1d7f980d4c02b080d87747297ee6/view.

About the Author

John Keller | Editor

John Keller is editor-in-chief of Military & Aerospace Electronics magazine, which provides extensive coverage and analysis of enabling electronic and optoelectronic technologies in military, space, and commercial aviation applications. A member of the Military & Aerospace Electronics staff since the magazine's founding in 1989, Mr. Keller took over as chief editor in 1995.

Voice your opinion!

To join the conversation, and become an exclusive member of Military Aerospace, create an account today!