Approximate reasoning based on fuzzy logic

  • Authors:

  • L. A. Zadeh

  • Affiliations:

  • Computer Science Division, Department of Electrical Engineering and Computer Sciences and the Electronics Research Laboratory, University of California, Berkeley, Berkeley, California

  • Venue:
  • IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1979

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Abstract

During the past several years, the emergence of expert systems as a field of considerable practical as well as theoretical importance within AI has provided a strong impetus for the develop ment of theories of approximate reasoning and credibility assessment of inference processes in knowledge-based systems. The approach to approximate reasoning described in this paper is based on a fuzzy logic, FL, in which the truth-values and quantifiers are defined as possibility distributions which carry linguistic labels such as true, quite true, not very true, many, not very many, several, almost all, etc. Based on the concept of a possibility distribution, a set of translation and Inference rules is developed and their application to inference from imprecise premises is illustrated by examples.