The Foundations of Rule-Based Computations in Fuzzy Models

  • Abraham Kandel
  • Roberto Pacheco
  • Alejandro Martins
  • Suresh Khator
Part of the International Series in Intelligent Technologies book series (ISIT, volume 7)


Since their inception, the main purpose of expert systems has been emulate human expert reasoning in problem solving. After the first developments, the researchers realized the importance of treating uncertainty in both data and knowledge. Fuzzy Expert Systems became a successful alternative due to their ability of modeling fuzziness of natural language as well as uncertainty in the information or in the knowledge (rules). Fuzzy Set Theory can model both kinds of imprecision while Fuzzy Logic provides the logical framework to the reasoning process. The foundations of fuzzy rule-based systems lie in two branches of Fuzzy Logic: Approximate and Possibilistic reasonings. In this chapter, we discuss the essential elements of these theories, alternative forms of fuzzy rule-based systems, and the relationship with other inexact reasoning techniques (Dempster-Shafer and Probability theories).


Fuzzy Expert Systems Approximate Reasoning Possibility Theory Uncertainty Treatment 


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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Abraham Kandel
    • 1
  • Roberto Pacheco
    • 2
  • Alejandro Martins
    • 2
  • Suresh Khator
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA
  2. 2.Dept. of Production EngineeringFederal University of Santa CatarinaFlorianópolisBrazil
  3. 3.Department of Industrial and Mgmt. Syst. EngineeringUniversity of South FloridaTampaUSA

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