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Discovery of Cellular Automata Rules Using Cases

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Discovery Science (DS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2843))

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Abstract

Cellular automata (CAs) are used for modeling the problem of adaptation in natural and artificial systems, but it is hard to design CAs having desired behavior. To support the task of designing CAs, this paper proposes a method for automatic discovery of cellular automata rules (CA-rules). Given a sequence of CA configurations, we first collect cellular changes of states as cases. The collected cases are then classified using a decision tree, which is used for constructing CA-rules. Conditions for classifying cases in a decision tree are computed using genetic programming. We perform experiments using several types of CAs and verify that the proposed method successfully finds correct CA-rules.

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References

  1. Adamatzky, A.: Identification of Cellular Automata. Taylor& Francis, London (1994)

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Author information

Authors and Affiliations

  1. Department of Computer and Communication Sciences, Wakayama University,  

    Ken-ichi Maeda & Chiaki Sakama

Authors
  1. Ken-ichi Maeda
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  2. Chiaki Sakama
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Editor information

Editors and Affiliations

  1. FG Knowledge Engineering, FB Informatik, Technical University Darmstadt, Hochschulstr. 10, 64289, Darmstadt

    Gunter Grieser

  2. Meme Media Laboratory, Hokkaido University, N13 W8, 0608628, Sapporo, Japan

    Yuzuru Tanaka

  3. Graduate School of Informatics, Kyoto University Yoshida Honmachi, Sakyo-ku, 606-850, Kyoto, Japan

    Akihiro Yamamoto

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© 2003 Springer-Verlag Berlin Heidelberg

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Maeda, Ki., Sakama, C. (2003). Discovery of Cellular Automata Rules Using Cases. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds) Discovery Science. DS 2003. Lecture Notes in Computer Science(), vol 2843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39644-4_34

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  • DOI: https://doi.org/10.1007/978-3-540-39644-4_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20293-6

  • Online ISBN: 978-3-540-39644-4

  • eBook Packages: Springer Book Archive

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