Skip to main content
SpringerLink
Log in

Spatial Inference — Learning vs. Constraint Solving

  • Conference paper
  • First Online:
KI 2002: Advances in Artificial Intelligence (KI 2002)

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

Included in the following conference series:

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

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. The Brandeis Interval Arithmetic Constraint Solver, March 2002. Available from http://www.cs.brandeis.edu/~tim/.

  2. A. P. Ambler and R. J. Popplestone. Inferring the Positions of Bodies from Specified Spatial Relationships. Artificial Intelligence, 6:157–174, 1975.

    Article  MATH  MathSciNet  Google Scholar 

  3. B. Claus, K. Eyferth, C. Gips, R. Hörnig, U. Schmid, S. Wiebrock, and F. Wysotzki. Reference Frames for Spatial Inferences in Text Comprehension. In C. Freksa, C. Habel, and K. F. Wender, editors, Spatial Cognition, LNAI 1404. Springer, 1998.

    Google Scholar 

  4. P. Geibel, C. Gips, S. Wiebrock, and F. Wysotzki. Learning Spatial Relations with CAL5 and TRITOP. Technical Report 98-7, TU Berlin, 1998.

    Google Scholar 

  5. P. Griebel, G. Lehrenfeld, W. Mueller, C. Tahedl, and H. Uhr. Integrating a Constraint Solver into a Real-Time Animation Environment. In Proc. of the 1996 IEEE Symposium on Visual Languages, September 1996.

    Google Scholar 

  6. H. W. Guesgen. Spatial Reasoning Based on Allen’s Temporal Logic. Technical Report TR-89-049, ICSI, Berkeley, Cal., 1989.

    Google Scholar 

  7. S. Haridi, S. Janson, J. Montelius, T. Franzen, P. Brand, K. Boortz, B. Danielsson, B. Carlson, T. Keisu, D. Sahlin, and T. Sjöland. Concurrent Constraint Programming at SICS with the Andorra Kernel Language. In P. Kanellakis, J.-L. Lassez, and V. Saraswat, editors, First Workshop on Principles and Practice of Constraint Programming-PPCP’93, 1993.

    Google Scholar 

  8. Daniel Hernández. Qualitative Representation of Spatial Knowledge. LNAI 804. Springer, 1994.

    Google Scholar 

  9. T.J. Hickey, M.H. van Emden, and H. Wu. A Unified Framework for Interval Constraints and Interval Arithmetic. In M. Maher and J.-F. Puget, editors, Principles and Practice of Constraint Programming-CP’98, LNCS 1520. Springer, 1998.

    Chapter  Google Scholar 

  10. P. Hofstedt. Better Communication for Tighter Cooperation. In First International Conference on Computational Logic, LNCS 1861. Springer, 2000.

    Google Scholar 

  11. P. Hofstedt, E. Godehardt, and D. Seifert. A Framework for Cooperating Constraint Solvers-A Prototypic Implementation. In E. Monfroy and L. Granvilliers, editors, Workshop on Cooperative Solvers in Constraint Programming-CoSolv, 2001.

    Google Scholar 

  12. P. N. Johnson-Laird. Mental Models: Towards a Cognitive Science of Language, Inference and Consciousness. Cambridge University Press, Cambridge, 1983.

    Google Scholar 

  13. T. Jörding and I. Wachsmuth. An Antropomorphic Agent for the Use of Spatial Language. In Proceedings of ECAI’96-Workshop on Representation and Processing of Spatial Expressions, pages 41–53, 1996.

    Google Scholar 

  14. G. Nakhaeizadeh and C. C. Taylor, editors. Machine Learning and Statistics-The Interface. Wiley, 1997.

    Google Scholar 

  15. T. Röfer. Routemark-based Navigation of a Wheelchair. In Third ECPD International Conference on Advanced Robotics, Intelligent Automation and Active Systems, Bremen, 1997.

    Google Scholar 

  16. S. Wiebrock, L. Wittenburg, U. Schmid, and F. Wysotzki. Inference and Visualization of Spatial Relations. In C. Freksa, W. Brauer, C. Habel, and K. Wender, editors, Spatial Cognition II, LNAI 1849. Springer, 2000.

    Google Scholar 

  17. S. Wiebrock and F. Wysotzki. Lernen von räumlichen Relationen mit CAL5 und DIPOL. Technical Report 99-17, TU Berlin, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Berlin University of Technology, Germany

    Carsten Gips, Petra Hofstedt & Fritz Wysotzki

Authors
  1. Carsten Gips
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petra Hofstedt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fritz Wysotzki
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44185-4

  • Online ISBN: 978-3-540-45751-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation