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Application of Pruning Techniques for Propositional Learning to Progol

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Inductive Logic Programming (ILP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2157))

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Abstract

Since learning with Inductive Logic Programming (ILP) can be regarded as the search problem through the hypotheses space, it is essential to reduce the search space in order to improve the efficiency. In the propositional learning framework, an efficient admissible search algorithm called OPUS (Optimized Pruning for Unordered Search) has been developed. OPUS employed the effective pruning techniques for unordered search and succeeded in improving the efficiency. In this paper, we propose an application of OPUS to an ILP system Progol. However, because of the difference of representation language, it is not applicable to ILP directly. We make the conditions clear under which the pruning techniques in OPUS can be applied in the framework of Progol. In addition, we propose a new pruning criterion, which can be regarded as inclusive pruning. Experiments are conducted to assess the effectiveness of the proposed algorithms. The results show that the proposed algorithms reduce the number of candidate hypotheses to be evaluated as well as the computational time for a certain class of problems.

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Author information

Authors and Affiliations

  1. Keio Research Institute at SFC, Japan

    Tomonobu Ozaki

  2. Graduate School of Media and Governance, Keio University, 5322 Endo, Fujisawa, Kanagawa, 252-8520, Japan

    Koichi Furukawa

Authors
  1. Tomonobu Ozaki
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  2. Koichi Furukawa
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Editor information

Editors and Affiliations

  1. Université Paris Sud, LRI, bât. 490, 91405, Orsay, France

    Céline Rouveirol

  2. Ecole Polytechnique, LMS, 91128, Palaiseau, France

    Michéle Sebag

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© 2001 Springer-Verlag Berlin Heidelberg

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Ozaki, T., Furukawa, K. (2001). Application of Pruning Techniques for Propositional Learning to Progol. In: Rouveirol, C., Sebag, M. (eds) Inductive Logic Programming. ILP 2001. Lecture Notes in Computer Science(), vol 2157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44797-0_17

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  • DOI: https://doi.org/10.1007/3-540-44797-0_17

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  • Print ISBN: 978-3-540-42538-0

  • Online ISBN: 978-3-540-44797-9

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