Advertisement

Applied Intelligence

, Volume 6, Issue 1, pp 49–58 | Cite as

Qualitative spatial reasoning using orientation, distance, and path knowledge

  • Kai Zimmermann
  • Christian Freksa
Article

Abstract

We give an overview of an approach to qualitative spatial reasoning based on directional orientation information as available through perception processes or natural language descriptions. Qualitative orientations in 2-dimensional space are given by the relation between a point and a vector. The paper presents our basic iconic notation for spatial orientation relations that exploits the structure of the spatial domain and explores a variety of ways in which these relations can be manipulated and combined for spatial reasoning. Using this notation, we explore a method for exploiting interactions between space and movement in this space for enhancing the inferential power. Finally, the orientation-based approach is augmented by distance information, which can be mapped into position constraints and vice versa.

Keywords

qualitative reasoning spatial reasoning constraint propagation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J.F. Allen, “Maintaining knowledge about temporal intervals,” Communications of the ACM, vol. 26, no. 11, pp. 832–843, 1983.Google Scholar
  2. 2.
    M. Egenhofer and R. Franzosa, “Point-set topological spatial relations,” International Journal of Geographical Information Systems, vol. 5, no. 2, pp. 161–174, 1991.Google Scholar
  3. 3.
    M. Egenhofer, “A formal definition of binary topological relationships,” in Proc. third International Conference on Foundations of Data Organization and Algorithms, 1989, pp. 457–472.Google Scholar
  4. 4.
    R.J. Elliott and M.E. Lesk, “Route finding in street maps by computers and people,” in Proc. AAAI, Pittsburgh, 1982, pp. 258–261.Google Scholar
  5. 5.
    A.U. Frank, “Qualitative spatial reasoning with cardinal directions,” in Proc. Seventh Austrian Conference on Artificial Intelligence, Vienna, 1991, pp. 157–167.Google Scholar
  6. 6.
    C. Freksa, “Qualitative Spatial Reasoning,” in Cognitive and Liguistic Aspects of Geographic Space, D.M. Mark and A.U. Frank (Eds.); Kluwer, Dordrecht, 1991, pp. 361–372.Google Scholar
  7. 7.
    C. Freksa, “Temporal reasoning based on semi-intervals,” Artificial Intelligence, vol. 54, pp. 199–227, 1992.Google Scholar
  8. 8.
    C. Freksa, “Using Orientation Information for Qualitative Spatial Reasoning,” in Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, A.U. Frank, I. Campari, and U. Formentini (Eds.), Springer, Berlin, 1992, pp. 162–178.Google Scholar
  9. 9.
    C. Freksa and K. Zimmermann, “On the utilization of spatial structures for cognitively plausible and efficient reasoning,” in Proc. IEEE International Conference on Systems, Man, and Cybernetics, Chicago, IL, 1992, pp. 261–266.Google Scholar
  10. 10.
    H.W. Güsgen, “Spatial reasoning based on Allen's temporal logic,” International Computer Science Institute, Berkeley, TR-89–049, 1989.Google Scholar
  11. 11.
    D. Hernández, “Diagrammatical aspects of qualitative representations of space,” in Proc. AAAI Spring Symposium on Reasoning with Diagrammatic Representations, Stanford, 1992, pp. 225–228.Google Scholar
  12. 12.
    B.J. Kuipers, “Representing knowledge of large scale space,” massachusetts Institute of Technology, Ph.D. Thesis, 1977.Google Scholar
  13. 13.
    B. Kuipers, “Modeling human knowledge of routes: partial knowledge and individual variation,” in Proc. AAAI, Washington, D.C., 1983, pp. 216–219.Google Scholar
  14. 14.
    L. Latecki and R. Röhrig, “Orientation and qualitative angle for spatial reasoning,” in Proc. IJCAI, Chambery, 1993, pp. 1544–1549.Google Scholar
  15. 15.
    T.S. Levitt, D.T. Lawton, D.M. Chelberg, and P.C. Nelson, “Qualitative landmark-based path planning and follwing,” in Proc. AAAI, Seattle, WA, 1987, pp. 689–694.Google Scholar
  16. 16.
    G.F. Ligozat, “Qualitative triangulation for spatial reasoning,” in Proc. International Conference on Spatial Information Theory. A Theoretical Basis for GIS, Elba, Italy, 1993, pp. 54–68.Google Scholar
  17. 17.
    A. Mukerjee and G. Joe, “A Qualitative Model for Space,” in Proc. AAAI, 1990, pp. 721–727.Google Scholar
  18. 18.
    D.A. Randell, Z. Cui and A. G. Cohn, “An interval logic for space based on ‘connection’,” in Proc. 10th European Conference on Artificial Intelligence, Vienna, Austria, 1992, pp. 394–398.Google Scholar
  19. 19.
    C. Schlieder, “Akquisition räumlichen Wissens am Beispiel ebener Sicht- und Anordnungsverhältnisse,” in Proc. Workshop Räumliche Alltagsumgebungen des Menschen, Koblenz-Landau, 1990, pp. 159–171.Google Scholar
  20. 20.
    K. Zimmermann and C. Freksa, “Enhancing spatial reasoning by the concept of motion,” in Proc. AISB, Birmingham, UK, 1993, pp. 140–147.Google Scholar
  21. 21.
    K. Zimmermann, “Enhancing qualitative spatial reasoning—combining orientation and distance,” in Proc. International Conference on Spatial Information Theory. A Theoretical Basis for GIS, Elba, Italy, 1993, pp. 69–76.Google Scholar
  22. 22.
    K. Zimmermann, “Measuring without measures—the Δ-calculus,” graduiertenkolleg Kognitionswissenschaft, Universität Hamburg, Report 39, 1994.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Kai Zimmermann
    • 1
  • Christian Freksa
    • 1
  1. 1.Department of Computer ScienceUniversity of HamburgHamburgGermany

Personalised recommendations