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Transferring as fast as possible the functioning of our brain to artificialintelligence is an ambitious goal that would help advance the state of the artin AI and robotics. It is in this perspective that we propose to start fromhypotheses derived from an empirical study in a human-robot interaction and toverify if they are validated in the same way for children as for a basicreinforcement learning algorithm. Thus, we check whether receiving help from anexpert when solving a simple close-ended task (the Towers of Hano\"i) allows toaccelerate or not the learning of this task, depending on whether theintervention is canonical or requested by the player. Our experiences haveallowed us to conclude that, whether requested or not, a Q-learning algorithmbenefits in the same way from expert help as children do.

Should artificial agents ask for help in human-robot collaborative problem-solving?