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L. A. Zadeh, “A Theory of Approximate Reasoning,” In: J. E. Hayes, D. Michie and L. I. Mikulich, Eds., Machine Intelligence, Vol. 9, Elsevier, New York, 1979, pp. 149-194.

has been cited by the following article:

• TITLE: Approximate Reasoning in Fuzzy Resolution

  AUTHORS: Banibrata Mondal, Swapan Raha

  KEYWORDS: Approximate Reasoning; Similarity Index; Similarity Based Reasoning; Resolution Principle

  JOURNAL NAME: International Journal of Intelligence Science, Vol.3 No.2, April 30, 2013

  ABSTRACT: Resolution is an useful tool for mechanical theorem proving in modelling the refutation proof procedure, which is mostly used in constructing a “proof” of a “theorem”. An attempt is made to utilize approximate reasoning methodology in fuzzy resolution. Approximate reasoning is a methodology which can deduce a specific information from general knowledge and specific observation. It is dependent on the form of general knowledge and the corresponding deductive mechanism. In ordinary approximate reasoning, we derive from A→B and by some mechanism. In inverse approximate reasoning, we conclude from A→B and using an altogether different mechanism. An important observation is that similarity is inherent in fuzzy set theory. In approximate reasoning methodology-similarity relation is used in fuzzification while, similarity measure is used in fuzzy inference mechanism. This research proposes that similarity based approximate reasoning-modelling generalised modus ponens/generalised modus tollens—can be used to derive a resolution—like inference pattern in fuzzy logic. The proposal is well-illustrated with artificial examples.