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Intrinsically Motivated Learning Systems: An Overview

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Intrinsically Motivated Learning in Natural and Artificial Systems

Abstract

This chapter introduces the field of intrinsically motivated learning systems and illustrates the content, objectives, and organisation of the book. The chapter first expands the concept of intrinsic motivations, then introduces a taxonomy of three classes of intrinsic-motivation mechanisms (based on predictors, on novelty detection, and on competence), and finally introduces and reviews the various contributions of the book. The contributions are organised in six parts. The contributions of the first part provide general overviews on the concept of intrinsic motivations, the possible mechanisms that may implement them, and the functions that they can play. The contributions of the second, third, and fourth part focus on the three classes of the aforementioned intrinsic-motivation mechanisms. The contributions of the fifth part discuss mechanisms that are complementary to intrinsic motivations. The contributions of the sixth part introduce tools and experimental paradigms that can be used to investigate intrinsic motivations.

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Acknowledgements

This paper and a large part of the research reported in this book have been supported by the project “IM-CLeVeR: Intrinsically Motivated Cumulative Learning Versatile Robots” funded by the European Commission under the 7th Framework Programme (FP7/2007-2013) and “Challenge 2: Cognitive Systems, Interaction, Robotics”, Grant Agreement No. ICT-IP-231722. Support or co-support from other institutions, where present, is described in the “Acknowledgment” section of each chapter. The editors of this book thank the EU reviewers (Benjamin Kuipers, Luc Berthouze, and Yasuo Kuniyoshi) and the EU project officer (Cécile Huet) for their valuable advices and their encouragement. For more information on the IM-CLeVeR project and for additional multimedia material, see the project website: http://www.im-clever.eu/. We also thank Simona Bosco for her editorial help with some contributions.

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  1. Institute of Cognitive Sciences and Technologies, CNR, Roma, Italy

    Gianluca Baldassarre & Marco Mirolli

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  1. Gianluca Baldassarre
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Correspondence to Gianluca Baldassarre .

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    Gianluca Baldassarre  & Marco Mirolli  & 

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Baldassarre, G., Mirolli, M. (2013). Intrinsically Motivated Learning Systems: An Overview. In: Baldassarre, G., Mirolli, M. (eds) Intrinsically Motivated Learning in Natural and Artificial Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32375-1_1

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  • DOI: https://doi.org/10.1007/978-3-642-32375-1_1

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