Abstract
This Ch. summarizes some common techniques of inference utilized for fuzzy data. Special attention has been paid to the implementation of modus ponens (which realizes a data-driven mode of reasoning) and modus tollens (corresponding to a goal-driven mode of reasoning). The detachment principle (corresponding to a means of expressing a similarity between fuzzy statements) is also investigated. We discuss how different forms of fuzzy relation equations are used to handle each of these modes of inference. Also the question of a direct link between the relevancy of the KB and the length of the inference chain leading to meaningful conclusions is considered. This is of primordial importance; it has to be analyzed to interpret the results of inference and, in particular, to visualize precision. A proper reformulation of the problem in terms of fuzzy equations makes it possible to consider this knowledge transformation in a greater detail.
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© 1989 Springer Science+Business Media Dordrecht
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di Nola, A., Sessa, S., Pedrycz, W., Sanchez, E. (1989). Inference Algorithms in Knowledge-Based Systems. In: Fuzzy Relation Equations and Their Applications to Knowledge Engineering. Theory and Decision Library, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1650-5_13
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DOI: https://doi.org/10.1007/978-94-017-1650-5_13
Publisher Name: Springer, Dordrecht
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