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Spatial Inference — Learning vs. Constraint Solving

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KI 2002: Advances in Artificial Intelligence (KI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2479))

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Abstract

We present a comparison of two new approaches for solving constraints occurring in spatial inference. In contrast to qualitative spatial reasoning we use a metric description, where relations between pairs of objects are represented by parameterized homogenous transformation matrices with numerical (nonlinear) constraints. We employ interval arithmetics based constraint solving and methods of machine learning in combination with a new algorithm for generating depictions for spatial inference

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Author information

Authors and Affiliations

  1. Berlin University of Technology, Germany

    Carsten Gips, Petra Hofstedt & Fritz Wysotzki

Authors
  1. Carsten Gips
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  2. Petra Hofstedt
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  3. Fritz Wysotzki
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Editor information

Editors and Affiliations

  1. Informatik V, RWTH Aachen, Ahornstraße 55, 52056, Aachen, Germany

    Matthias Jarke  & Gerhard Lakemeyer  & 

  2. Computer Science Research, IBM Research Laboratory, Säumerstraße 4, 8803, Rüschlikon, Switzerland

    Jana Koehler

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© 2002 Springer-Verlag Berlin Heidelberg

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Gips, C., Hofstedt, P., Wysotzki, F. (2002). Spatial Inference — Learning vs. Constraint Solving. In: Jarke, M., Lakemeyer, G., Koehler, J. (eds) KI 2002: Advances in Artificial Intelligence. KI 2002. Lecture Notes in Computer Science(), vol 2479. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45751-8_20

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  • DOI: https://doi.org/10.1007/3-540-45751-8_20

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44185-4

  • Online ISBN: 978-3-540-45751-0

  • eBook Packages: Springer Book Archive

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