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Spatial Inference — Learning vs. Constraint Solving

  • Carsten Gips
  • Petra Hofstedt
  • Fritz Wysotzki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2479)

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

Keywords

Spatial Relation Constraint Program Interval Arithmetic Generalization Error Spatial Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Carsten Gips
    • 1
  • Petra Hofstedt
    • 1
  • Fritz Wysotzki
    • 1
  1. 1.Berlin University of TechnologyGermany

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