Summary. Incomplete information is a problem in many aspects of actual environments. Furthermore, in many scenarios the knowledge is not represented in a crisp way. It is common to find fuzzy concepts or problems with some level of uncertainty. There are not many practical systems which handle fuzziness and uncertainty and the few examples that we can find are used by a minority. To extend a popular system (which many programmers are using) with the ability of combining crisp and fuzzy knowledge representations seems to be an interesting issue.
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© 2007 Springer-Verlag Berlin Heidelberg
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Munoz-Hernandez, S., Vaucheret, C. (2007). Fuzzy Prolog: Default Values to Represent Missing Information. In: Kaburlasos, V.G., Ritter, G.X. (eds) Computational Intelligence Based on Lattice Theory. Studies in Computational Intelligence, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72687-6_14
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DOI: https://doi.org/10.1007/978-3-540-72687-6_14
Publisher Name: Springer, Berlin, Heidelberg
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