Uncertainty models in information and database systems
Journal of Information Science
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
A linguistic approach to temporal information analysis
A linguistic approach to temporal information analysis
Test-score semantics for natural languages
COLING '82 Proceedings of the 9th conference on Computational linguistics - Volume 1
A knowledge-based technique for constraints satisfaction in manpower allocation
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
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It is a truism that much of human reasoning is approximate in nature. Spatial reasoning is an area where humans consistently reason approximately with demonstrably good results. The unique mental processes behind these actions are not well understood. However, it is important that we try to incorporate such approximate reasoning techniques in our computer systems. Approximate spatial reasoning is very important for intelligent mobile agents (e.g., robots), specially for those operating in uncertain or unknown or dynamic domains. In such situations, besides the hazard in the domain, the real constraints (e.g., limited memory and limited time for observations and/or inferencing) faced by agents makes the use of approximate reasoning techniques imperative. In this paper we present a model for approximate spatial reasoning using fuzzy logic to represent the uncertainty in the environment. We develop algorithms to reason about spatial information expressed in the form of approximate linguistic descriptions, very similar to the kind of spatial information processed by humans. We only deal with static spatial reasoning in this paper.