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. 2018 Aug 13;5:88. doi: 10.3389/frobt.2018.00088

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Copyright © 2018 Chatila, Renaudo, Andries, Chavez-Garcia, Luce-Vayrac, Gottstein, Alami, Clodic, Devin, Girard and Khamassi.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Evolution of monitored signals when both experts are controlling the robot during the navigation task. (Top row) shows the sliding mean cost spent by both experts for decision-making. (Middle row) shows the measures of learning scaled by their coefficient. (Bottom row) shows the evolution of the probability of selection of each expert. In these experiments, the strong parameter of the habitual expert learning measure combined with its slow convergence favors the goal-directed selection in order to reach the goal more easily (however at a high computational cost).