AI bests Air Force combat tactics experts in simulated dogfights

Retired United States Air Force Colonel Gene Lee, in a flight simulator, takes on the ALPHA AI. It doesn't go well for him. (credit: Lisa Ventre, University of Cincinnati)

In the future, the US Air Force hopes to have armed drones flying in formation with human pilots, responding to their verbal and digital commands to fight the enemy and strike targets. That would require an artificial intelligence capable of interpreting commands and applying knowledge of combat tactics—something that is already being proven in a project funded by the Air Force Research Lab.

ALPHA, an artificial intelligence trained by a retired Air Force expert in air combat, was originally developed as what amounts to ultimate video game AI—an autonomous simulated enemy for use in training fighter pilots. The AI is so good that it has consistently beaten human pilots in simulated air combat—even when heavily handicapped by simulated physics. And now AFRL is investigating using ALPHA as the AI for Unmanned Combat Aerial Vehicles (UCAVs) in the physical world, potentially flying missions alongside human pilots.

Described in a paper recently published in the Journal of Defense Management, ALPHA was created using a "genetic fuzzy tree" (GFT) system. There's a lot to unpack in that term, but in short, the methodology uses genetic algorithms—code intended to mimic evolution and natural selection—to train a collection of independent but interconnected "fuzzy inference systems" (FISs). Instead of training each bit of fuzzy logic independently for a given task, as is normally done in fuzzy systems, the genetic algorithm "is utilized to train each system in the Fuzzy Tree simultaneously," lead researcher Nick Ernest, CEO of Psibernetix Inc. (the company that developed ALPHA) and his co-authors wrote in the paper. "Each FIS has membership functions that classify the inputs and outputs into linguistic classifications, such as 'far away' and 'very threatening', as well as if-then rules for every combination of inputs, such as 'If missile launch computer confidence is moderate and mission kill shot accuracy is very high, fire missile'. By breaking up the problem into many sub-decisions, the solution space is significantly reduced."

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