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Learning Qualitative Models of Physical and Biological Systems

  • Chapter
Computational Discovery of Scientific Knowledge

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4660))

Abstract

We present a qualitative model-learning system, Qoph, developed for application to scientific discovery problems. Qoph learns the structural relations between a set of observed variables. It has been shown capable of learning models with intermediate (unmeasured) variables, and intermediate relations, under different levels of noise, and from qualitative or quantitative data. A biological application of Qoph is explored. An additional significant outcome of this work is the discovery and identification of kernel subsets of key states that must be present for model-learning to succeed.

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Authors and Affiliations

  1. Department of Computer Science, University of Wales, Aberystwyth, United Kingdom

    Simon M. Garrett & Ross D. King

  2. Department of Computing Science, University of Aberdeen, Aberdeen, United Kingdom

    George M. Coghill

  3. Computing Laboratory, Oxford University, Oxford, United Kingdom

    Ashwin Srinivasan

Authors
  1. Simon M. Garrett
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  2. George M. Coghill
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  3. Ashwin Srinivasan
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  4. Ross D. King
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Sašo Džeroski Ljupčo Todorovski

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Garrett, S.M., Coghill, G.M., Srinivasan, A., King, R.D. (2007). Learning Qualitative Models of Physical and Biological Systems. In: Džeroski, S., Todorovski, L. (eds) Computational Discovery of Scientific Knowledge. Lecture Notes in Computer Science(), vol 4660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73920-3_12

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  • DOI: https://doi.org/10.1007/978-3-540-73920-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73919-7

  • Online ISBN: 978-3-540-73920-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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