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Tacit Learning – Machine Learning Paradigm Based on the Principles of Biological Learning

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Intelligent Assistive Robots

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 106))

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Abstract

Adaptations to unpredictable environmental changes enable living organisms to survive in their natural environments and are therefore the highest-priority tasks for all of them. In the long history of evolution, living organisms have developed regulatory systems that can adapt their activities to the environment and, as a result have been able to extend their activity fields to almost all places on the earth.

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Author information

Authors and Affiliations

  1. Autonomous Behavioral Control unit, RIKEN, BSI-TOYOTA collaboration center, 2271-130, Anagahora, Shimo-shidami, Moriyama-ku Nagoya, Aichi, 463-0003, Japan

    Shingo Shimoda

Authors
  1. Shingo Shimoda
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Corresponding author

Correspondence to Shingo Shimoda .

Editor information

Editors and Affiliations

  1. LiSSi Laboratory, University Paris-Est/UPEC, Vitry-sur-Seine, France

    Samer Mohammed

  2. Bioengineering Group (GBIO), Spanish National Research Council (CSIC), Madrid, Spain

    Juan C. Moreno

  3. Department of Mechanical Engineering Robotic Systems Control Laboratory, Sogang University, Seoul, Korea, Republic of (South Korea)

    Kyoungchul Kong

  4. LiSSi Laboratory, University Paris-Est/UPEC, Vitry-sur-Seine, France

    Yacine Amirat

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Shimoda, S. (2015). Tacit Learning – Machine Learning Paradigm Based on the Principles of Biological Learning. In: Mohammed, S., Moreno, J., Kong, K., Amirat, Y. (eds) Intelligent Assistive Robots. Springer Tracts in Advanced Robotics, vol 106. Springer, Cham. https://doi.org/10.1007/978-3-319-12922-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-12922-8_8

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12921-1

  • Online ISBN: 978-3-319-12922-8

  • eBook Packages: EngineeringEngineering (R0)

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