Should artificial agents ask for help in human-robot collaborative problem-solving?
This work addresses how artificial agents can learn from human help in collaborative problem-solving, but it is incremental as it applies a basic reinforcement learning method to a new human-robot interaction context.
The study investigated whether receiving help from an expert accelerates learning in a simple task (Towers of Hanoi) for both children and a Q-learning algorithm, finding that help, whether requested or not, benefits both similarly.
Transferring as fast as possible the functioning of our brain to artificial intelligence is an ambitious goal that would help advance the state of the art in AI and robotics. It is in this perspective that we propose to start from hypotheses derived from an empirical study in a human-robot interaction and to verify if they are validated in the same way for children as for a basic reinforcement learning algorithm. Thus, we check whether receiving help from an expert when solving a simple close-ended task (the Towers of Hanoï) allows to accelerate or not the learning of this task, depending on whether the intervention is canonical or requested by the player. Our experiences have allowed us to conclude that, whether requested or not, a Q-learning algorithm benefits in the same way from expert help as children do.