L. Ughetto, D. Dubois and H. Prade, “Implicative and Conjunctive Fuzzy Rule—A Tool for Reasoning from Knowledge and Examples,” Proceedings of the 16th National Conference on Artificial Intelligence and the 11th Innovative Applications of Artificial Intelligence Conference, American Association for Artificial Intelligence, Menlo Park, 1999, pp. 214-219.

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.