AI vs. Human Fighter Pilot: Here's Who Won the Epic Dogfight
An AI developed by Heron Systems beat four others to challenge a human F-16 pilot.
The dogfight is part of a DARPA-led effort to develop AI for fighter planes.
The AI won an important string of victories but robotic air combat still has a ways to go.
An artificial intelligence claimed victory over several other AIs in simulated dogfights and then went on to trounce a real human Air Force pilot, beating him 5-0.
The Heron Systems-developed AI beat algorithms from Lockheed Martin and Aurora Flight Sciences and eventually advanced to a final matchup against a skilled District of Columbia Air National Guard pilot. Ironically, the AI’s lack of a formal air combat education likely contributed to the win.
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The round robin tournament was the culmination of a yearlong effort by the Defense Advanced Research Projects Agency (DARPA). Known as AlphaDogFight, the effort recruited several defense contractors, startups, and university laboratories to develop an artificial intelligence capable of dogfighting in a F-16 Fighting Falcon jet.
On Friday, August 21, companies like Heron, Lockheed, and Aurora, as well as institutions like Georgia Tech, participated in a lightning series of simulated dogfights. When it was all over, Heron's AI pulled ahead of the pack, racking up the most kills. Here's a recording of the live broadcast:
The final dogfight of the event was between the Heron Systems AI and “Banger,” an F-16 pilot assigned to the Washington D.C. Air National Guard and a veteran of the Air Force’s Weapon Instructor Course. Heron beat Banger five times in a row.
The Heron vs. Banger dogfights took place after a “merge,” in which two opposing fighter jets race toward one another head-on. The duels took place in JSBSim, a multi-platform open source flight dynamics model, with Banger utilizing a VR headset. The duel involved using the F-16’s onboard M61 Vulcan 20-millimeter Gatling gun only, with no missiles allowed. According to the rules of engagement, gun shots were assumed to automatically hit the target, with no misses.
Heron’s AI was very aggressive throughout the tournament, quickly maneuvering into position to get a shot off against its opponent. The AI made the most of the F-16’s flight characteristics, position, and speed in ways that trained pilots might not, often to execute some shots that were unlikely to score hits in real life, but were registered as “kills” in-tournament.
This is particularly apparent in the duel against Banger. While the pilot took time to set up a shot with a high kill probability, the Heron AI went for any shot it could conceivably take. In each case, Heron reached a position where it could get a shot off faster than Banger could—but in reality, Banger might have actually won the duel.
Heron’s victory is a landmark win for artificial intelligence in warfare, and it highlights the advantages of AI-controlled weapons systems. Unencumbered with a human body that feels disorientation, discomfort, and even pain, the AI pushed its virtual F-16 to greater limits, including higher g-forces, to position itself for a kill—any kill. Those physical factors simply don’t exist for an AI.
Air combat AI still has a long way to go, though.
In the future, artificial intelligence will have to deal with engagements where a kill is not guaranteed. If a specific maneuver could lead to the AI being in position with only a 30 percent probability of kill, should it take it, or should it continue seeking an advantage in the dogfight in hopes of a higher probability of kill in the future? How will an AI dogfight with missiles?
The AI, C4ISRNet points out, had “perfect intelligence” in the duels, meaning it knew everything about its opponent and the engagement. How does an AI deal with air combat when it knows substantially less, and how does it learn about an adversary during the engagement itself?
Here’s aviation YouTuber (and former F-16 pilot) C.W. Lemoine providing commentary over the AlphaDogFight trials:
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