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

Adrien Bennetot (1, 2) , Vicky Charisi (3) , Natalia Díaz-Rodríguez (4, 2)

1 SEGULA Technologies [Trappes]
2 U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
3 JRC - European Commission - Joint Research Centre [Seville]
4 Flowers - Flowing Epigenetic Robots and Systems

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.

Domaines: Intelligence artificielle [cs.AI]

Adrien Bennetot, Vicky Charisi, Natalia Díaz-Rodríguez. Should artificial agents ask for help in human-robot collaborative problem-solving?. Brain-PIL Workshop - ICRA2020, Jun 2020, Paris, France. ⟨hal-02871356⟩