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Computational Revision of Quantitative Scientific Models

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Discovery Science (DS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2226))

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Abstract

Research on the computational discovery of numeric equations has focused on constructing laws from scratch, whereas work on theory revision has emphasized qualitative knowledge. In this paper, we describe an approach to improving scientific models that are cast as sets of equations. We review one such model for aspects of the Earth ecosystem, then recount its application to revising parameter values, intrinsic properties, and functional forms, in each case achieving reduction in error on Earth science data while retaining the communicability of the original model. After this, we consider earlier work on computational scientific discovery and theory revision, then close with suggestions for future research on this topic.

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Author information

Authors and Affiliations

  1. NTT Communication Science Laboratories, 2-4 Hikaridai, Seika, 619-0237, Soraku, Kyoto, Japan

    Kazumi Saito

  2. Computational Learning Laboratory, CSLI, Standford University, Standford, California, 94305, USA

    Pat Langley & Trond Grenager

  3. Ecosystem Science and Technology Branch, NASA Ames Research Center, MS 242-4, Moffett Field, California, 94035, USA

    Christopher Potter, Alicia Torregrosa & Steven A. Klooster

Authors
  1. Kazumi Saito
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  2. Pat Langley
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  3. Trond Grenager
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  4. Christopher Potter
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  5. Alicia Torregrosa
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  6. Steven A. Klooster
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Editor information

Editors and Affiliations

  1. DFKI GmbH Saarbrücken, 66123, Saarbrücken, Germany

    Klaus P. Jantke

  2. Department of Informatics, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, 812-8581, Fukuoka, Japan

    Ayumi Shinohara

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© 2001 Springer-Verlag Berlin Heidelberg

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Saito, K., Langley, P., Grenager, T., Potter, C., Torregrosa, A., Klooster, S.A. (2001). Computational Revision of Quantitative Scientific Models. In: Jantke, K.P., Shinohara, A. (eds) Discovery Science. DS 2001. Lecture Notes in Computer Science(), vol 2226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45650-3_29

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  • DOI: https://doi.org/10.1007/3-540-45650-3_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42956-2

  • Online ISBN: 978-3-540-45650-6

  • eBook Packages: Springer Book Archive

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