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Fluent Learning: Elucidating the Structure of Episodes

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Advances in Intelligent Data Analysis (IDA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2189))

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Abstract

Fluents are logical descriptions of situations that persist, and composite fluents are statistically significant temporal relationships between fluents.T his paper presents an algorithm for learning composite fluents incrementally from categorical time series data.Th e algorithm is tested with a large dataset of mobile robot episodes.I t is given no knowledge of the episodic structure of the dataset (i.e., it learns without supervision) yet it discovers fluents that correspond well with episodes.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts, USA

    Paul R. Cohen

Authors
  1. Paul R. Cohen
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Editor information

Editors and Affiliations

  1. Royal Institute of Technology, Centre for Autonomous Systems, 10044, Stockholm, Sweden

    Frank Hoffmann

  2. Imperial College, Huxley Building 180 Queen’s Gate, London, SW7 2BZ, UK

    David J. Hand  & Niall Adams  & 

  3. Department of Computer Science, Vanderbilt University, Box 1679, Station B, Nashville, TN, 37235, USA

    Douglas Fisher

  4. Department of Computer Science, New University of Lisbon, 2825-114, Caparica, Portugal

    Gabriela Guimaraes

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© 2001 Springer-Verlag Berlin Heidelberg

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Cohen, P.R. (2001). Fluent Learning: Elucidating the Structure of Episodes. In: Hoffmann, F., Hand, D.J., Adams, N., Fisher, D., Guimaraes, G. (eds) Advances in Intelligent Data Analysis. IDA 2001. Lecture Notes in Computer Science, vol 2189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44816-0_27

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  • DOI: https://doi.org/10.1007/3-540-44816-0_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42581-6

  • Online ISBN: 978-3-540-44816-7

  • eBook Packages: Springer Book Archive

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