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Distributional Learning of Regular Formal Graph System of Bounded Degree

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Inductive Logic Programming (ILP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10326))

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Abstract

In this paper, we describe how distributional learning techniques can be applied to formal graph system (FGS) languages. An FGS is a logic program that deals with term graphs instead of the terms of first-order predicate logic. We show that the regular FGS languages of bounded degree with the 1-finite context property (1-FCP) and bounded treewidth property can be learned from positive data and membership queries.

T. Shoudai—This work was partially supported by JSPS KAKENHI (26280087, 15K00313) and MEXT KAKENHI (24106010).

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Author information

Authors and Affiliations

  1. Faculty of International Studies, Kyushu International University, Kitakyushu, Japan

    Takayoshi Shoudai

  2. Faculty of Science, Tokai University, Hiratsuka, Japan

    Satoshi Matsumoto

  3. Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan

    Yusuke Suzuki

Authors
  1. Takayoshi Shoudai
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  2. Satoshi Matsumoto
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  3. Yusuke Suzuki
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Corresponding author

Correspondence to Takayoshi Shoudai .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, York, United Kingdom

    James Cussens

  2. Imperial College London, London, United Kingdom

    Alessandra Russo

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Shoudai, T., Matsumoto, S., Suzuki, Y. (2017). Distributional Learning of Regular Formal Graph System of Bounded Degree. In: Cussens, J., Russo, A. (eds) Inductive Logic Programming. ILP 2016. Lecture Notes in Computer Science(), vol 10326. Springer, Cham. https://doi.org/10.1007/978-3-319-63342-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-63342-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63341-1

  • Online ISBN: 978-3-319-63342-8

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