The US Air Force Test Pilot School and the Defense Advanced Research Projects Agency (DARPA) have achieved a significant breakthrough in machine learning by demonstrating that AI software can fly a modified F-16 fighter jet in a dogfight against human pilots. This achievement is based on the implementation of machine learning in an X-62A VISTA, a plane built as a testbed as it can mimic the performance of other aircraft.
The potential for autonomous air-to-air combat has been imaginable for decades, but the reality has remained a distant dream up until now. In 2023, the X-62A broke one of the most significant barriers in combat aviation.
DARPA has been testing AI agent software for piloting simulated planes for several years. Its Air Combat Evolution (ACE) program dates back to 2020, when AlphaDogfight trials pitted human pilots in a flight simulator against an AI opponent. The AI software won that competition but had an edge – it was allowed to fly at speeds that would have overstressed a real aircraft and generated g-forces that would harm a human pilot.
The machine learning approach relies on analyzing historical data to make informed decisions for both present and future situations, often discovering insights that are imperceptible to humans or challenging to express through conventional rule-based languages. Machine learning is extraordinarily powerful in environments and situations where conditions fluctuate dynamically making it difficult to establish clear and robust rules.
However, machine learning has a downside that needs to be overcome. It needs to be explainable and verifiable enough that military personnel will trust it and that aviation authorities will certify systems that implement such code. The X-62A is essentially an F-16 that’s been integrated with a flight simulator, so that machine learning agents can operate the plane.
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