ALPHA, whose destiny was once just to help train fighter pilots in simulated exercises, has proved to be so good that the Air Force Research Laboratory (AFRL) is investigating whether the bot could provide the AI blueprint for combat drones in the future, potentially flying missions alongside human pilots.
"ALPHA is an incredibly difficult opponent to face," its developer Nick Earnest, CEO of Psibernetix told Digital Trends.
"Even flying against other pilots when ALPHA has severe handicaps to a number of its systems — including speed, turning, missile capability and sensors — it is able to win," Earnest said.
"There is additional work to be done to both increase ALPHA's capabilities and improve its model fidelity — but these results represent a significant breakthrough," said Earnest, University of Cincinnati graduate said.
ALPHA was created using a Genetic Fuzzy Tree (GFT) system that uses genetic algorithms to train independent and interconnected systems.
The genetic algorithm, "is utilized to train each system in the Fuzzy Tree simultaneously," Nick Earnest said in an article published in the Journal of Defense Management. The code trains interconnected "fuzzy inference systems," (FISs).
Another acronym: GFT — Genetic Fuzzy Tree. A subtype of fuzzy logic algorithms.— Uwe Pleban (@uwepleban) June 28, 2016
Earnest, who now runs Psibernetix Inc — which developed ALPHA — and his co-authors wrote:
"Each FIS has membership functions that classify the inputs and outputs into linguistic classifications, such as 'far away' and 'very threatening,' as well as if-then rules for every combination of inputs, such as 'if missile launch computer confidence is moderate and mission kill shot accuracy is very high, fire missile.'
"By breaking up the problem into many sub-decisions, the solution space is significantly reduced."
Essentially, the development and success of ALPHA in virtual combat roles elevates the question whether AI will replace the need for skilled combat pilots to the top of the virtual agenda.
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