Located conveniently near Wright-Patterson AFB, Psibernetix is honored to provide our AI solutions to the Department of Defense for the complex problems our armed forces face. We are currently working with United States Air Force to develop Intelligent Systems for Unmanned Aerial Vehicle control. Our extremely effective and robust methods are prime candidates for these types of complex problems requiring real-time control in the face of many different uncertainties.
Our work with the USAF began with first GFT developed by Psibernetix CEO Nick Ernest; the Learning Enhanced Tactical Handling Algorithm. This AI, developed as part of a Dayton Area Graduate Studies Institute fellowship, seeks to autonomously handle planning and control of defensive systems aboard fighter aircraft / Unmanned Combat Aerial Vehicles.
Flagship Defense AI: ALPHA
ALPHA is an Artificial Intelligence that controls flights of Unmanned Combat Aerial Vehicles in aerial combat missions within a high-fidelity simulation environment. To date, ALPHA is the world’s most complex application of a fuzzy-logic based Artificial Intelligence.
Psibernetix was hired to create ALPHA by the US Air Force Research Laboratory (AFRL). The ultimate goal of this project is increasing autonomous capabilities to allow mixed combat teams of manned and unmanned air fighters to operate in highly contested environments. For the time being, ALPHA controls the red forces in a mission within the AFSIM simulation environment against a blue opposing force to serve as a training tool.
Just as UAVs represented a revolutionary capability for the USAF in the mid-1990s, Manned-Unmanned Autonomous Teaming in an air combat environment will certainly represent a revolutionary leap in capability of airpower in the near future. Air combat, as it is performed by human pilots today, is a highly dynamic application of aerospace physics, skill, art, and intuition to maneuver a fighter aircraft and missile against an adversary moving at high speeds in three dimensions. Today’s fighters close on each other at speeds in excess of 1,500 MPH while flying at altitudes above 40,000 feet. The selection and application of air-to-air tactics requires assessing a tactical advantage or disadvantage and reacting appropriately in microseconds. The cost of mistakes is high. Future aircraft are likely to employ a high level of coordinated autonomous offensive and defensive capabilities, requiring reaction times which surpass that of a human pilot, in order to survive in such hostile environments.
ALPHA’s red forces are handicapped with shorter range missiles and a reduced missile payload than the blue opposing forces. ALPHA also does not have Airborne Warning and Control System (AWACS) support providing 360° long range radar coverage of the area; the blue forces do. The aircraft for both teams are identical in terms of their mechanical performance. Both to mirror training exercises and to offset these weaknesses, ALPHA is typically given a numeric advantage over the blue forces.
Figured above: side-view during active combat. Past and current missile detonation locations marked. Two Blue vs. four Red, all Reds have successfully evaded missiles, one Blue has been destroyed, Blue AWACS in distance.
Trained by our EVE learning system, ALPHA has been able to achieve immense success against a baseline controller AFRL had previously been utilizing to control the red forces. However, ALPHA’s ability to defeat AI-flown enemies is only one measure of success; it must also be able to defeat highly trained and experienced fighter pilots. ALPHA was assessed by an air combat subject matter expert, Colonel (retired) Gene “Geno” Lee. As a former USAF Air Battle Manager, Mr. Lee is a United States Air Force Fighter Weapon School graduate and Adversary Tactics (Aggressor) Instructor, and has controlled or flown in thousands of air-to-air intercepts as a Ground Control Intercept officer, as a Mission Commander on AWACS, and in the cockpit of multiple fighter aircraft. Psibernetix and Geno worked together to develop tactics, techniques, and procedures to overcome ALPHA’s payload and no-AWACS disadvantage, capitalize on blue’s mistakes, and take advantage of numeric platform superiority (when the situation presented itself).
When Geno took manual control of the blue aircraft against the reds controlled by the baseline controller AFRL had previously been utilizing, he could easily defeat it. However, even after repeated attempts against ALPHA, not only could he not score a kill against it, he was shot out of the air by the reds every time after protracted engagements. He described ALPHA as “the most aggressive, responsive, dynamic and credible AI (he’s) seen-to-date.”
Distribution approved for public release; 88ABW Cleared 12/15/2015; 88ABW-2015-6028