His team developed a genetic fuzzy logic called Alpha capable of shooting down human pilots in simulations, even when the computer’s aircraft intentionally was handicapped with a slower top speed and less nimble flight characteristics. The system’s autonomous real-time decision-making shot down retired U.S. Air Force Col. Gene Lee in every engagement.



“It seemed to be aware of my intentions and reacting instantly to my changes in flight and my missile deployment,” Lee said last year. “It knew how to defeat the shot I was taking. It moved instantly between defensive and offensive actions as needed.”

The American Institute of Aeronautics and Astronautics honored Cohen and Ernest this year for their “advancement and application of artificial intelligence to large scale, meaningful and challenging aerospace-related problems.”



Cohen spent much of his career working with fuzzy-logic based AI in drones. He used a sabbatical from the engineering college to approach the UC College of Medicine with an idea: What if they could apply the amazing predictive power of fuzzy logic to a particularly nettlesome medical problem?



Medicine and avionics have little in common. But each entails an ordered process — a vast decision tree — to arrive at the best choices. Fuzzy logic is a system that relies not on specific definitions but generalizations to compensate for uncertainty or statistical noise. This artificial intelligence is called “genetic fuzzy” because it constantly refines its answer, tossing out the lesser choices in a way analogous to the genetic processes of Darwinian natural selection.



Cohen compares it to teaching a child how to recognize a chair. After seeing just a few examples, any child can identify the object people sit in as a chair, regardless of its shape, size or color.



“We do not require a large statistical database to learn. We figure things out. We do something similar to emulate that with fuzzy logic,” Cohen said.