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Novelty and Beyond: Towards Combined Motivation Models and Integrated Learning Architectures

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Intrinsically Motivated Learning in Natural and Artificial Systems

Abstract

For future intrinsically motivated agents to combine multiple intrinsic motivation or behavioural components, there is a need to identify fundamental units of motivation models that can be reused and combined to produce more complex agents. This chapter reviews three existing models of intrinsic motivation, novelty, interest and competence-seeking motivation, that are based on the neural network framework of a real-time novelty detector. Four architectures are discussed that combine basic units of the intrinsic motivation functions in different ways. This chapter concludes with a discussion of future directions for combined motivation models and integrated learning architectures.

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Authors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, Australian Defence Force Academy, Northcott Drive, Canberra, ACT, Australia

    Kathryn E. Merrick

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  1. Kathryn E. Merrick
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Correspondence to Kathryn E. Merrick .

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  1. Istituto di Scienze e Tecnologie della Cognizione Consiglio Nazionale delle Ricerche, Rome, Italy

    Gianluca Baldassarre  & Marco Mirolli  & 

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Merrick, K.E. (2013). Novelty and Beyond: Towards Combined Motivation Models and Integrated Learning Architectures. 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_9

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  • DOI: https://doi.org/10.1007/978-3-642-32375-1_9

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  • Online ISBN: 978-3-642-32375-1

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