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Intelligent robotic systems: Adaptation, learning, and evolution

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Abstract

Living creatures have evolved and formed ecological systems by adapting to their dynamic environment. Robots also need an adaptability to the dynamic environment. This paper presents methodologies for adaptation, learning, and evolution in robotics. Further the intelligence of a robot emerges as a result of the synthesis of simultaneous processing of perception, decision making, and action. A robotic system requires the whole structure of intelligence, and acquires skill and knowledge through interaction with the dynamic environment by computational intelligence, including neural networks, fuzzy systems, and genetic algorithms.

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Authors and Affiliations

  1. Center for Cooperative Research in Advanced Science and Technology, Department of Mechano-Informatics and Systems, and Department of Micro System Engineering, Nagoya University, 1 Furo-cho, Chikusa-ku, 464-01, Nagoya, Japan

    Toshio Fukuda

  2. Department of Mechanical Engineering, Osaka Institute of Technology, 535, Osaka, Japan

    Naoyuki Kubota

Authors
  1. Toshio Fukuda
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  2. Naoyuki Kubota
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Fukuda, T., Kubota, N. Intelligent robotic systems: Adaptation, learning, and evolution. Artif Life Robotics 3, 32–38 (1999). https://doi.org/10.1007/BF02481485

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  • DOI: https://doi.org/10.1007/BF02481485

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