Dr Roderich Gross from the Department of Automatic Control and Systems Engineering at the University of Sheffield, said: “Our study uses the Turing test to reveal how a given system works. In our case, we put a swarm of robots under surveillance and wanted to find out which rules caused their movements. To do so, we put a second swarm - made of learning robots - under surveillance too. The movements of all the robots were recorded, and the motion data shown to interrogators.”
He added: “Unlike in the original Turing test, however, our interrogators are not human but rather computer programs that learn by themselves. Their task is to distinguish between robots from either swarm. They are rewarded for correctly categorising the motion data from the original swarm as genuine, and those from the other swarm as counterfeit. The learning robots that succeed in fooling an interrogator, by making it believe their motion data were genuine, receive a reward.”
Dr Gross explained the advantage of the approach, called ‘Turing Learning’, is that humans no longer need to tell machines what to look for.
The discovery could be used to create algorithms that detect abnormalities in behaviour. This could prove useful for the health monitoring of livestock and for the preventive maintenance of machines, cars and airplanes.
Turing Learning could also be used in security applications, such as for lie detection or online identity verification.
