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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 1996: Simulated Evolution and Learning pp 177–186Cite as

Knowledge extraction using neural network by an artificial life approach

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1285))

Abstract

A novel knowledge extraction method from autonomous behavior of multiple mobile robots by an artificial life approach is proposed in this paper. The knowledge is expressed by if-then rules and we employ a neural network for the knowledge extraction. The proposed method has the following features: 1)The structure of knowledge extraction neural network and the learning algorithm are simple; 2)Understanding and modification of the extracted knowledge are easy because weights in the knowledge extraction network directly represent the antecedents and the consequents of the if-then rules; 3)The network itself has an ability of inference using the extracted knowledge. We used a lot of autonomous mobile robots in various environments. Each robot has to avoid the obstacles to get to the goal and the local behavior is extracted and integrated in the knowledge extraction neural network as global knowledge. We confirmed the validity of the proposed method by computer simulations.

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Authors and Affiliations

  1. Department of Electrical Engineering, Keio University, 3-14-1 Hiyoshi, 223, Kohoku-ku, Yokohama, Japan

    Yuji Makita & Masafurni Hagiwara

Authors
  1. Yuji Makita
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  2. Masafurni Hagiwara
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Xin Yao Jong-Hwan Kim Takeshi Furuhashi

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© 1997 Springer-Verlag Berlin Heidelberg

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Makita, Y., Hagiwara, M. (1997). Knowledge extraction using neural network by an artificial life approach. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028534

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  • DOI: https://doi.org/10.1007/BFb0028534

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63399-0

  • Online ISBN: 978-3-540-69538-7

  • eBook Packages: Springer Book Archive

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