It is every Top Gun's worst nightmare - an AI can can outmanoeuvre them in the air.

Now researchers have tested their AI on a retired top gun - and left him stunned.

Retired United States Air Force Colonel Gene Lee took on the AI in a simulator - and lost.

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The end of Top Guns? An AI has beated Air Force pilots in simulated showdowns for the first time.

A COMPREHENSIVE VICTORY Retired United States Air Force Colonel Gene Lee took on the AI in a simulator. Not only was Lee not able to score a kill against ALPHA after repeated attempts, he was shot out of the air every time during protracted engagements in the simulator. Advertisement

The Artificial intelligence (AI) developed by a University of Cincinnati doctoral graduate was recently assessed by Lee - who holds extensive aerial combat experience as an instructor and Air Battle Manager with considerable fighter aircraft expertise.

He took on the software in a simulator.

The artificial intelligence, dubbed Alpha, was the victor in that simulated scenario, and according to Lee, is 'the most aggressive, responsive, dynamic and credible AI I've seen to date.'

Details on Alpha- a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management.

The system is designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes.

Alpha is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

In its earliest iterations, it consistently outperformed a baseline computer program previously used by the Air Force Research Lab for research.

In fact, it was only after early iterations of ALPHA bested other computer program opponents that Lee then took to manual controls against a more mature version of ALPHA last October.

Not only was Lee not able to score a kill against ALPHA after repeated attempts, he was shot out of the air every time during protracted engagements in the simulator.

Since that first human vs. ALPHA encounter in the simulator, this AI has repeatedly bested other experts as well, and is even able to win out against these human experts when its aircraft are deliberately handicapped in terms of speed, turning, missile capability and sensors.

Lee, who has been flying in simulators against AI opponents since the early 1980s, said of that first encounter against Alpha, 'I was surprised at how aware and reactive it was.

Retired United States Air Force Colonel Gene Lee, in a flight simulator, takes part in simulated air combat versus artificial intelligence technology developed by a team comprised of industry, US Air Force and University of Cincinnati representatives.

'It seemed to be aware of my intentions and reacting instantly to my changes in flight and my missile deployment. It knew how to defeat the shot I was taking.

'It moved instantly between defensive and offensive actions as needed.'

He added that with most AIs, 'an experienced pilot can beat up on it (the AI) if you know what you're doing.

'Sure, you might have gotten shot down once in a while by an AI program when you, as a pilot, were trying something new, but, until now, an AI opponent simply could not keep up with anything like the real pressure and pace of combat-like scenarios.'

But, now, it's been Lee, who has trained with thousands of U.S. Air Force pilots, flown in several fighter aircraft and graduated from the U.S. Fighter Weapons School (the equivalent of earning an advanced degree in air combat tactics and strategy), as well as other pilots who have been feeling pressured by ALPHA.

And, anymore, when Lee flies against ALPHA in hours-long sessions that mimic real missions, 'I go home feeling washed out. I'm tired, drained and mentally exhausted.

'This may be artificial intelligence, but it represents a real challenge.'

HOW IT WORKS: THE FUZZY TREE SYSTEM The AI uses what's called a 'Genetic Fuzzy Tree' (GFT) system, a subtype of what's known as fuzzy logic algorithms. Genetic fuzzy systems have been shown to have high performance, and a problem with four or five inputs can be solved handily. That's where the Genetic Fuzzy Tree system and Cohen and Ernest's years' worth of work come in. According to Psibernetix, recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm, 'the easiest way I can describe the Genetic Fuzzy Tree system is that it's more like how humans approach problems. 'Take for example a football receiver evaluating how to adjust what he does based upon the cornerback covering him. 'The receiver doesn't think to himself: 'During this season, this cornerback covering me has had three interceptions, 12 average return yards after interceptions, two forced fumbles, a 4.35 second 40-yard dash, 73 tackles, 14 assisted tackles, only one pass interference, and five passes defended, is 28 years old, and it's currently 12 minutes into the third quarter, and he has seen exactly 8 minutes and 25.3 seconds of playtime.'' 'That receiver - rather than standing still on the line of scrimmage before the play trying to remember all of the different specific statistics and what they mean individually and combined to how he should change his performance - would just consider the cornerback as 'really good.'' Retired United States Air Force Colonel Gene Lee, in a flight simulator, takes part in simulated air combat versus artificial intelligence technology developed by a team comprised of industry, US Air Force and University of Cincinnati representatives. 'The cornerback's historic capability wouldn't be the only variable. 'Specifically, his relative height and relative speed should likely be considered as well. 'So, the receiver's control decision might be as fast and simple as: 'This cornerback is really good, a lot taller than me, but I am faster.' 'At the very basic level, that's the concept involved in terms of the distributed computing power that's the foundation of a Genetic Fuzzy Tree system wherein, otherwise, scenarios/decision making would require too high a number of rules if done by a single controller.' The programming involved breaking up the complex challenges and problems represented in aerial fighter deployment into many sub-decisions, thereby significantly reducing the required 'space' or burden for good solutions. The branches or sub divisions of this decision-making tree consists of high-level tactics, firing, evasion and defensiveness. That's the 'tree' part of the term 'Genetic Fuzzy Tree' system. Advertisement

The AI is so fast that it could consider and coordinate the best tactical plan and precise responses, within a dynamic environment, over 250 times faster than its human opponents could blink.

UC's Cohen added, 'Alpha would be an extremely easy AI to cooperate with and have as a teammate.

'It could continuously determine the optimal ways to perform tasks commanded by its manned wingman, as well as provide tactical and situational advice to the rest of its flight.'

Alpha and its algorithms require no more than the computing power available in a low-budget PC in order to run in real time and quickly react and respond to uncertainty and random events or scenarios.

According to a lead engineer for autonomy at AFRL, 'ALPHA shows incredible potential, with a combination of high performance and low computational cost that is a critical enabling capability for complex coordinated operations by teams of unmanned aircraft.'

To reach its current performance level, ALPHA's training has occurred on a $500 consumer-grade PC.

This training process started with numerous and random versions of ALPHA.

These automatically generated versions of ALPHA proved themselves against a manually tuned version of ALPHA.

The successful strings of code are then 'bred' with each other, favoring the stronger, or highest performance versions. In other words, only the best-performing code is used in subsequent generations. Eventually, one version of ALPHA rises to the top in terms of performance, and that's the one that is utilized.