Abstract
Fuzzy reasoning is a very productive research field that during the last years has provided a number of theoretical approaches and practical implementation prototypes. Nevertheless, the classical implementations, like Fril, are not adapted to the latest formal approaches, like multi-adjoint logic semantics.
Some promising implementations, like Fuzzy Prolog, are so general that the regular user/programmer does not feel comfortable because either the representation of fuzzy concepts is complex or the results of the fuzzy queries are difficult to interpret.
In this paper we present a modern framework, RFuzzy, that is modeling multi-adjoint logic in a practical way. It provides some extensions as default values (to represent missing information), partial default values (for a subset of data) and typed variables. RFuzzy represents the truth value of predicates using facts, rules and also can define fuzzy predicates as continuous functions. Queries are answered with direct results (instead of providing complex constraints), so it is easy to use for any person that wants to represent a problem using fuzzy reasoning in a simple way (just using the classical fuzzy representation with real numbers). The most promising characteristic of RFuzzy is that the user can obtain constructive answers to queries that restrict the truth value.
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Munoz-Hernandez, S., Ceruelo, V.P., Strass, H. (2009). RFuzzy: An Expressive Simple Fuzzy Compiler. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_34
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DOI: https://doi.org/10.1007/978-3-642-02478-8_34
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