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Title: Learning the rules of a game: neural conditioning in human-robot interaction with delayed rewards
Authors: Soltoggio, Andrea
Reinhart, Felix
Lemme, Andre
Steil, Jochen
Issue Date: 2013
Publisher: © IEEE
Citation: SOLTOGGIO, A. ... et al., 2013. Learning the rules of a game: neural conditioning in human-robot interaction with delayed rewards. IN: IEEE 3rd Joint International Conference on Development and Learning and Epigenetic Robotics, ICDL 2013 - Electronic Conference Proceedings, 18-22 August 2013, 6pp.
Abstract: Learning in human-robot interaction, as well as in human-to-human situations, is characterised by noisy stimuli, variable timing of stimuli and actions, and delayed rewards. A recent model of neural learning, based on modulated plasticity, suggested the use of rare correlations and eligibility traces to model conditioning in real-world situations with uncertain timing. The current study tests neural learning with rare correlations in a human-robot realistic teaching scenario. The humanoid robot iCub learns the rules of the game rock-paper-scissors while playing with a human tutor. The feedback of the tutor is often delayed, missing, or at times even incorrect. Nevertheless, the neural system learns with great robustness and similar performance both in simulation and in robotic experiments. The results demonstrate the efficacy of the plasticity rule based on rare correlations in implementing robotic neural conditioning.
Description: This is a conference paper © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: This work was supported by the European Communitys Seventh Framework Programme FP7/2007–2013, Challenge 2 Cognitive Systems, Interaction, Robotics under grant agreement No 248311 - AMARSi.
Version: Accepted for publication
DOI: 10.1109/DevLrn.2013.6652572
URI: https://dspace.lboro.ac.uk/2134/16076
Publisher Link: http://dx.doi.org/10.1109/DevLrn.2013.6652572
Appears in Collections:Conference Papers and Presentations (Computer Science)

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