Abstract
The way we use spatial descriptions in many everyday situations is of a qualitative nature. This is often achieved by specifying spatial relations between objects or regions. The advantage of using qualitative descriptions is that we can be less precise and thereby less prone to making an error. For example, it is often easier to decide whether an object is inside another object than to specify exactly where the first object is with respect to the second one. In artificial intelligence, a variety of formalisms have been developed that deal with space on the basis of relations between objects or regions that objects might occupy. One of these formalisms is the RCC theory, which is based on a primitive relation, called connectedness, and uses a set of topological relations, defined on the basis of connectedness, to provide a framework for reasoning about regions. This paper discusses an extension of the RCC theory based on fuzzy logic, which enables us to express preferences among spatial descriptions.
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Guesgen, H.W. (2015). A Fuzzy Set Approach to Expressing Preferences in Spatial Reasoning. In: Eiter, T., Strass, H., Truszczyński, M., Woltran, S. (eds) Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation. Lecture Notes in Computer Science(), vol 9060. Springer, Cham. https://doi.org/10.1007/978-3-319-14726-0_12
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DOI: https://doi.org/10.1007/978-3-319-14726-0_12
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