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Qualitative spatial reasoning: A semi-quantitative approach using fuzzy logic

  • Spatial Reasoning
  • Conference paper
  • First Online:
Design and Implementation of Large Spatial Databases (SSD 1989)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 409))

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Abstract

Qualitative reasoning is useful as it facilitates reasoning with incomplete and weak information and aids the subsequent application of more detailed quantitative theories. Adoption of qualitative techniques for spatial reasoning can be very useful in situations where it is difficult to obtain precise informationand where there are real constraints of memory, time and hostile threats. This paper formulates a computational model for obtaining all induced spatial constraints on a set of landmarks, given a set of approximate quantitative and qualitative constraints on them, which may be incomplete, and perhaps even conflicting.

This research has been supported by the grants NASA-NSS-2-275 and AFOSR-89-0084.

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References

  1. B. J. Kuipers and T. S. Levitt, “Navigation and Mapping in Large-Scale Space,” AI Magazine, 1988.

    Google Scholar 

  2. P. Hayes, “The Naive Physics Manifesto,” Expert Systems in the Micro-electronic Age, vol. D. Michie, Ed., Edinburgh University Press, Edinburgh, U.K., 1979.

    Google Scholar 

  3. P. Hayes, “Naive Physics 1: Ontology for Liquids,” Formal Theories of the Commonsense World, vol. J.R. Hobbs and R. Moore, Eds., pp. 71–107, Norwood, NJ, 1985.

    Google Scholar 

  4. K. Forbus, “Intelligent Computer-Aided Engineering,” AI Magazine, vol. 9, pp. 23–26, 1988.

    Google Scholar 

  5. K. Forbus, “Qualitative Physics: Past Present and Future,” Exploring Artificial Intelligence, vol. H. Shrobe, Ed., Morgan Kaufmann, Los Altos, CA, 1989.

    Google Scholar 

  6. J. de Kleer, “Qualitative and Quantitative Knowledge in Classical Mechanics,” Tech. Report 352, MIT AI Lab, 1975.

    Google Scholar 

  7. J. de Kleer and J. Brown, “A Qualitative Physics Based on Confluences,” Artificial Intelligence, vol. 24, pp. 7–84, 1984.

    Article  Google Scholar 

  8. B. Kuipers, “Qualitative Simulation,” Artificial Intelligence, vol. 29, pp. 289–338, 1986.

    Article  Google Scholar 

  9. B. J. Kuipers, “Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge,” Automatica, July 1989.

    Google Scholar 

  10. B. D. D'Ambrosio, “Qualitative Process Theory Using Linguistic Variables,” Ph.D. Dissertation, University of California-Berkeley, Computer Science Dept., 1984.

    Google Scholar 

  11. L. A. Zadeh, “The Concept of a Linguistic Variable and its Application to Approximate Reasoning,” Information Sciences, vol. Part I, 8, 199–249; Part II, 8, 301–357; Part III, 9, 43–80, 1975.

    Article  Google Scholar 

  12. C. K. Yap, “Algorithmic Motion Planning,” Advances in Robotics Vol. I: Algorithmic and Geometric Aspects of Robotics, (J.T. Schwartz and C.K. Yap, Eds.), pp. 95–144, Lawrence Erlbaum Associated, Hillsdale, N.J., 1987.

    Google Scholar 

  13. N. S. V. Rao, “Algorithmic Framework for learned robot navigation in unknown terrains,” Computer, vol. 22(6), pp. 37–43, June 1989.

    Article  Google Scholar 

  14. A. Abelson and A. diSessa, Turtle Geometry, pp. 179–199, MIT Press, 1980.

    Google Scholar 

  15. V.J. Lumelsky and A. A. Stepanov, “Path planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape,” Algorithmica, vol. 2, pp. 403–430, 1987.

    Article  Google Scholar 

  16. J. B. Oommen et. al., “Robot navigation in unknown terrains using learned visibility graphs, Part I: The disjoint convex obstacle case,” IEEE Journal of robotics and automation, vol. RA-12, pp. 672–681, 1987.

    Google Scholar 

  17. A. Elfes, “Using Occupancy Grids for mobile robot perception and navigation,” Computer, vol. 22(6), pp. 46–57, June 1989.

    Article  Google Scholar 

  18. B. J. Kuipers and Y. T. Byun, “A Qualitative Approach to Robot Exploration and Maplearning,” Spatial Reasoning and Multi-sensor fusion, Proc. of 1987 workshop, pp. 390–404, Morgan Kaufmann Publishers Inc., Los Altos, CA, 1987.

    Google Scholar 

  19. R. A. Brooks, “Visual Map Making for a Mobile Robot,” Proc. of IEEE Intl. Conf. on Robotics and Automation, pp. 824–829, 1985.

    Google Scholar 

  20. R. A. Brooks, “A Robust Layered Control System for a Mobile Robot,” IEEE Journal of Robotics and Automation, vol. RA-2, No. 1, pp. 14–23, 1986.

    Google Scholar 

  21. K. D. Forbus, “Spatial and Qualitative Aspects of Reasoning About Motion,” Proc. of AAAI, 1980.

    Google Scholar 

  22. T. Levitt, D. Lawton, D. Chelberg, and P. Nelson, “Qualitative Navigation,” Proc. of DARPA Image Understanding Workshop, pp. 447–465, Morgan Kaufmann, Los Altos, CA, 1987.

    Google Scholar 

  23. D. McDermott, “Finding Objects with Given Spatial Properties,” Tech. Report 195, Yale Univ. Computer Science Dept., 1980.

    Google Scholar 

  24. D. McDermott and Ernest Davis, “Planning and Executing Routes through Uncertain Territory,” Artificial Intelligence, vol. 22, pp. 107–156, 1984.

    Article  Google Scholar 

  25. E. Davis, Representing and Acquiring Geographic Knowledge, Morgan Kauffman/Pitman, Los Altos, CA/London, U.K., 1986.

    Google Scholar 

  26. H. J. Zimmerman, Fuzzy Set Theory and its Applications, Kluwer Academic Publishers Group, 1985.

    Google Scholar 

  27. A. Kaufmann and M. M. Gupta, Introduction to Fuzzy Arithmetic: Theory and Applications, Van Nostrand Reinhold Co., New York, NY, 1985.

    Google Scholar 

  28. L. A. Zadeh, “Test Score Semantics for Natural Languages and Meaning Representation via PRUF,” Empirical Semantics (B. Reiger, Ed.), pp. 281–349, Bochum: Brockmeyer, 1982.

    Google Scholar 

  29. L. T. Kozlowski and K. J. Bryant, “Sense of Direction, Spatial Orientation and Cognitive Maps,” Journal of Experimental Psychology: Human Perception and Performance, vol. 3, pp. 590–598, 1977.

    Article  Google Scholar 

  30. R. N. Shepard and J. Metzler, “Mental Rotation of Three-Dimensional Objects,” Science, vol. 171, pp. 701–703, 1971.

    PubMed  Google Scholar 

  31. W. Pylyshyn, Computation and Cognition: Toward a Foundation for Cognitive Science, MIT Press, Cambridge, MA, 1984.

    Google Scholar 

  32. H. Schone, Spatial Orientation-The Spatial Control of Behavior in Animals and Man, Princeton University Press, Princeton, NJ, 1984.

    Google Scholar 

  33. D. McDermott and A. Gelsey, “Terrain Analysis for Tactical Situation Assessment,” Spatial Reasoning and Multi-sensor Fusion, Proc. of 1987 workshop, pp. 420–429, Morgan Kaufmann, Los Altos, CA, 1987.

    Google Scholar 

  34. N. Foreman and R. Stevens, “Relationships between the Superior Colliculus and Hippocampus: Neural and Behavioral Considerations,” Behavioral and Brain Sciences, vol. 10 (1), pp. 101–151, 1987.

    Google Scholar 

  35. E. Hutchins, “Understaning Micronesian Navigation,” Mental models, D. Gentner and A. L. Stevens, Eds., pp. 191–225, Lawrence Erlbaum Associates, 1983.

    Google Scholar 

  36. J. McReynolds, “Geographic Orientation of the Blind,” Ph.D. dissertation in U.T., 1951.

    Google Scholar 

  37. J. Piaget and B. Inhelder, The Child's Perception of Space, Norton, New York, 1967.

    Google Scholar 

  38. S. Dutta, “Approximate reasoning about time and space in dynamic classification problems,” Ph. D. dissertation, Dept. of Computer Science, U. C. Berkeley, Berkeley, CA, 1989.

    Google Scholar 

  39. D. Dubois and H. Prade, Possibility Theory: An approach to computerized processing of uncertainty, Plenum Press, New York, 1988.

    Google Scholar 

  40. R. L. Sheng, “A Linguistic Approach to Temporal Information Analysis,” Ph.D. Thesis, University of California-Berkeley, Computer Science Dept., 1983.

    Google Scholar 

  41. S. Baase, Computer Algorithms: Introduction to Design and Analysis, Addison-Wesley, Reading, MA, 1978.

    Google Scholar 

  42. O. Gunther, “An Expert Data-base System for the Overland Search Problem,” M.S. Report, Dept. of Computer Science, UC Berkeley, 1985.

    Google Scholar 

  43. M. Stonebraker, et al., “The Design and Implementation of INGRES,” ACM Transactions on Database Systems, vol. 1, No. 3, pp. 189–222, Sep. 1976.

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, University of California, 94720, Berkeley, CA

    Soumitra Dutta

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  1. Soumitra Dutta
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Editor information

Alejandro P. Buchmann Oliver Günther Terence R. Smith Yuan-Fang Wang

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© 1990 Springer-Verlag Berlin Heidelberg

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Dutta, S. (1990). Qualitative spatial reasoning: A semi-quantitative approach using fuzzy logic. In: Buchmann, A.P., Günther, O., Smith, T.R., Wang, YF. (eds) Design and Implementation of Large Spatial Databases. SSD 1989. Lecture Notes in Computer Science, vol 409. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-52208-5_36

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  • DOI: https://doi.org/10.1007/3-540-52208-5_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-52208-9

  • Online ISBN: 978-3-540-46924-7

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