Computers are seen normally as perfect machines that follow fixed rules to get the best ''secure'' return, but what happens if it is impossible to get a perfect strategy with the information they have? Will machines learn to lie to compensate the lack of information?
I trained two different AI's as observers with a Q-learning algorithm and a neural network to play a simple "min-max" game, in this game there is the possibility to lie but it has a risk factor associated.
This two different AIs got different results making the Q-learning algorithm lie about 30% of the time and the neural network lying less than 3% of the matches.