Abstract
This paper deals with different kinds of plausible reasoning in the framework of fuzzy set1 and possibility theories: deductive reasoning in the presence of imprecise or uncertain premises, “proximity” reasoning, default reasoning, analogical reasoning, combination of uncertain or imprecise information from different sources. The modeling of the relative importance and of the mutual dependency of different preconditions with respect to a conclusion is examined. All the issues are discussed from a knowledge engineering point of view.2
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References
A fuzzy set is used here as a set or subset with unsharp boundaries. In a fuzzy set, the transition between membership and non-membership is gradual rather than abrupt. The concept of a fuzzy set was introduced by L. A. Zadeh, 1965. Many words in natural languages have a fuzzy meaning and express fuzzy properties.
Knowledge engineering is defined here as the applied branch of artificial intelligence aiming to mechanize a part of human reasoning and to process expert and task-oriented knowledge on computers.
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The term possibility distribution is taken here as an assessment of [0,l]-valued degrees of possibility to a set of alternatives. A fuzzy set is represented by a characteristic function which assesses a degree of membership to each element. This membership function may be viewed as a possibility distribution restricting the possible values of a variable known to take its value in the fuzzy set.
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© 1987 D. Reidel Publishing Company, Dordrecht, Holland
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Prade, H. (1987). Some Issues in Approximate and Plausible Reasoning in the Framework of a Possibility Theory-Based Approach. In: Vaina, L.M. (eds) Matters of Intelligence. Synthese Library, vol 188. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3833-5_12
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