Abstract
In this chapter we present the theory of L-sets as a tool for handling vagueness. A thorough analysis of the vague data provides us with well defined semantics. It turns out that vagueness has to be conceived as imprecision with respect to different contexts. Due to this interpretation L-sets have to be distinguished from fuzzy sets although there are various formal similarities. Finally we address the evaluation of vague data by the use of the concepts of possibility and necessity, and show how to apply these techniques in practice.
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© 1991 Springer-Verlag Berlin Heidelberg
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Kruse, R., Schwecke, E., Heinsohn, J. (1991). Vague Data. In: Uncertainty and Vagueness in Knowledge Based Systems. Artificial Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76702-9_3
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DOI: https://doi.org/10.1007/978-3-642-76702-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-76704-3
Online ISBN: 978-3-642-76702-9
eBook Packages: Springer Book Archive