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The Autotelic Principle

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 3139)

Abstract

The dominant motivational paradigm in embodied AI so far is based on the classical behaviorist approach of reward and punishment. The paper introduces a new principle based on ’flow theory’. This new, ‘autotelic’, principle proposes that agents can become self-motivated if their target is to balance challenges and skills. The paper presents an operational version of this principle and argues that it enables a developing robot to self-regulate its development.

Keywords

  • Reward Function
  • Operation Phase
  • Rock Climber
  • Steady Performance
  • Total Agent

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2004 Springer-Verlag Berlin Heidelberg

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Steels, L. (2004). The Autotelic Principle. In: Iida, F., Pfeifer, R., Steels, L., Kuniyoshi, Y. (eds) Embodied Artificial Intelligence. Lecture Notes in Computer Science(), vol 3139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27833-7_17

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  • DOI: https://doi.org/10.1007/978-3-540-27833-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22484-6

  • Online ISBN: 978-3-540-27833-7

  • eBook Packages: Springer Book Archive