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Abstraction and Phase Transitions in Relational Learning

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Abstraction, Reformulation, and Approximation (SARA 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1864))

Abstract

Computational complexity is often a major obstacle to the application of AI techniques to significant real-world problems. Efforts are then required to understand the sources of this complexity, in order to tame it without introducing, if possible, too strong simplifications that make either the problem or the technique useless.

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Author information

Authors and Affiliations

  1. Universitá del Piemonte Orientale Dipartimento di Scienze e Tecnologie Avanzate, Corso Borsalino 54, 15100, Alessandria, Italy

    Lorenza Saitta

  2. Laboratoire d’Informatique de Paris 6, Université Paris VI - CNRS, 4, Place Jussieu, F-75252, Paris, France

    Jean-Daniel Zucker

Authors
  1. Lorenza Saitta
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  2. Jean-Daniel Zucker
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nebraska - Lincoln, 115 Ferguson Hall, Lincoln, NE, 68588-0115

    Berthe Y. Choueiry

  2. Department of Computer Science, University of York, Heslington, York, Y010 5DD, UK

    Toby Walsh

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© 2000 Springer-Verlag Berlin Heidelberg

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Saitta, L., Zucker, JD. (2000). Abstraction and Phase Transitions in Relational Learning. In: Choueiry, B.Y., Walsh, T. (eds) Abstraction, Reformulation, and Approximation. SARA 2000. Lecture Notes in Computer Science(), vol 1864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44914-0_19

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  • DOI: https://doi.org/10.1007/3-540-44914-0_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67839-7

  • Online ISBN: 978-3-540-44914-0

  • eBook Packages: Springer Book Archive

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