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Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination

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Multi-Objective Machine Learning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 16))

Abstract

This chapter discusses the behavioral learning of robots from the viewpoint of multiobjective design. Various coordination methods for multiple behaviors have been proposed to improve the control performance and to manage conflicting objectives. We proposed various learning methods for neuro-fuzzy controllers based on evolutionary computation and reinforcement learning. First, we introduce the supervised learning method and evolutionary learning method for multiobjective design of robot behaviors. Then, the multiobjective design of fuzzy spiking neural networks for robot behaviors is presented. The key point behind these methods is to realize the adaptability and reusability of behaviors through interactions with the environment.

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Author information

Authors and Affiliations

  1. Department of System Design, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji, Tokyo, 192-0397, Japan

    Naoyuki Kubota

  2. PRESTO, Japan Science and Technology Agency, Japan

    Naoyuki Kubota

Authors
  1. Naoyuki Kubota
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Editors and Affiliations

  1. Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, Offenbach, 63073, Germany

    Yaochu Jin Dr.

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© 2006 Springer

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Kubota, N. (2006). Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination. In: Jin, Y. (eds) Multi-Objective Machine Learning. Studies in Computational Intelligence, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33019-4_24

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  • DOI: https://doi.org/10.1007/3-540-33019-4_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30676-4

  • Online ISBN: 978-3-540-33019-6

  • eBook Packages: EngineeringEngineering (R0)

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