Approximate spatial reasoning: integrating qualitative and quantitative constraints
International Journal of Approximate Reasoning
Qualitative spatial reasoning: the CLOCK project
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
The challenge of qualitative spatial reasoning
ACM Computing Surveys (CSUR)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Qualitative representation of positional information
Artificial Intelligence
A method of spatial reasoning based on qualitative trigonometry
Artificial Intelligence
Imprecise reasoning in geographic information systems
Fuzzy Sets and Systems - Special issue on Uncertainty in geographic information systems and spatial data
Maintaining knowledge about temporal intervals
Communications of the ACM
ACM SIGMOD Record
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
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Qualitative models of spatial knowledge concern the description of both objects and their relative position in space. In this context, fuzzy knowledge concerns the degree of likelihood with which a qualitative spatial relation can be mapped to a geometric counterpart. In this chapter, we argue that the integration of fuzzy knowledge into qualitative models allows us more effective spatial reasoning. In fact, the basic step of reasoning with positional relations, that is, the composition of two relations, if iterated over a path of several intermediate positions, introduces too much indeterminacy in the result. If the algorithms for composition take into account fuzzy knowledge, the latter effect is considerably reduced, obtaining an indication of the degree of likelihood of the result.