Advertisement

Handling Fuzziness in Knowledge-Based Systems

  • Antonio di Nola
  • Salvatore Sessa
  • Witold Pedrycz
  • Elie Sanchez
Chapter
Part of the Theory and Decision Library book series (TDLD, volume 3)

Abstract

In this Ch., as well as in the following, we will study a unified approach for handling and processing sources of uncertainty in knowledge-based systems. This goal is achieved in the framework of fuzzy relation equations. We point out how the mechanisms of the theory developed in the previous Chs. of this book can be treated as a convenient platform for construction of knowledge-based systems. More precisely, it will be indicated how fuzzy equations contribute to each of the conceptual levels recognized in the construction of these systems (viz. knowledge representation, meta-knowledge, inference techniques, etc.) as well as how they are directly used in formation of the particular elements of the problem-oriented expert systems. It is assumed that the reader has a certain background concerning Knowledge Engineering and Artificial Intelligence, at least on fundamentals of architecture of knowledge-based systems. It is also expected that he is familiar with some of the well-known expert systems, especially those broadly documented in literature (e.g. PROSPECTOR, MYCIN) and mechanisms involved there which are capable of coping with uncertainty, no matter how it has been introduced. This Ch. must be viewed as a concise prerequisite for the successive Chs. and it indicates many problems occurring in knowledge engineering when factors of uncertainty have to be processed.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    R. Beyth-Maroin, How probable is probable? Numerical translation of verbal probability expressions, J. of Forecasting 1 (1982), 257–269.CrossRefGoogle Scholar
  2. [2]
    P.P. Bonissone, Summarizing and propagating uncertain information with triangular norms, J. Approx. Reasoning 1 (1987), 71–101.MathSciNetCrossRefGoogle Scholar
  3. [3]
    P.P. Bonissone and K.S. Decker, Selecting uncertainty calculi and granularity, in: Uncertainty in Artificial Intelligence ( L. Kanal and J. Lemmer, Eds.), North-Holland, New York (1986), pp. 2217–2247.Google Scholar
  4. [4]
    A.L. Brown, Modal propositional semantics for reason maintenance system, Proceedings of the 9th Internat. Conference on Artificial Intelligence, Los Angeles, California, 1985.Google Scholar
  5. [5]
    P.R. Cohen and M.R. Grinberg, A framework for heuristics reasoning about uncertainty, Proceedings of the 8th Internat. Joint Conference on Artificial Intelligence, Karlsruhe, West Germany (1983), 355–357.Google Scholar
  6. [6]
    J. Fox, D.C. Barber and K.D. Bardhar, Alternative to Bayes? A quantitative comparison with rule-based diagnostic inference, Method of Information in Medicine 19 (1980), 210–215.Google Scholar
  7. [7]
    M.R. Genesereth, An Overview of MRS for AI Experts, Stanford Heuristic Programming Project, Report n. HPP-82–27, Dept. of Computer Science, Stanford Univ., 1982.Google Scholar
  8. [8]
    M.L. Ginsberg, Non-monotonic reasoning using Dempster’s rule, Proceedings of the National Conference on Artificial Intelligence, Austin, Texas, 1984, 126–129.Google Scholar
  9. [9]
    M.L. Ginsberg, Implementing Probabilistic Reasoning, Stanford Heuristic Programming Project, Report n. HPP-84–31, Dept. of Computer Science, Stanford Univ., 1984.Google Scholar
  10. [10]
    J.A. Goguen, The logic of inexact concepts, Synthese 19 (1986), 325–379.CrossRefGoogle Scholar
  11. [11]
    D.A. McAllester, An Outlook on Truth Maintenance, MIT Artificial Intelligence Laboratory, Cambridge, Mass., 1980.Google Scholar
  12. [12]
    D. McDermott, Non-monotonic logic II: Non-monotonic modal theories, J. Assn. Comp. Machinery 29 (1982), 33–57.MathSciNetzbMATHCrossRefGoogle Scholar
  13. [13]
    L. Philips and W. Edwards, Conservatism in a simple probability inference task, J. Exp. Psychology 72 (1966), 346–354.CrossRefGoogle Scholar
  14. [14]
    J.R. Quinlan, INFERNO: A cautious approach to uncertain inference, Computer J. 26 (1983), 255–269.CrossRefGoogle Scholar
  15. [15]
    P. Szolovitz and S.G. Pauker, Categorical and probabilistic reasoning in medical diagnosis, Artificial Intelligence 11 (1978), 115–144.CrossRefGoogle Scholar
  16. [16]
    D.A. Waterman, A guide to Expert Systems, Addison-Wesley, Reading, Mass., 1986.Google Scholar
  17. [17]
    L.A. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and System 1 (1978), 3–28.MathSciNetzbMATHCrossRefGoogle Scholar
  18. [18]
    L.A. Zadeh, Fuzzy sets and information granularity, in: Advances in Fuzzy Set Theory and Applications ( M.M. Gupta, R.K. Ragade and R.R. Yager, Eds.), North-Holland, Amsterdam (1979), pp. 3–18.Google Scholar
  19. [19]
    L.A. Zadeh, The role of fuzzy logic in the management of uncertainty in expert systems, Fuzzy Sets and Systems 11 (1983), 199–227.MathSciNetzbMATHCrossRefGoogle Scholar
  20. [20]
    A C Zimmer, The estimation of subjective probabilities via categorical judgements of uncertainty, in: Uncertainty in Artificial Intelligence ( L. Kanal and J. Lemmer, Eds.), North-Holland, New York (1986), pp. 249–258.Google Scholar
  21. [21]
    R. Zwick, E. Carlstein and D.V. Budescu, Measures of similarity among fuzzy concepts: a comparative analysis, Internat. J. Approx. Reasoning 1 (1987), 221–242.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1989

Authors and Affiliations

  • Antonio di Nola
    • 1
  • Salvatore Sessa
    • 1
  • Witold Pedrycz
    • 2
  • Elie Sanchez
    • 3
  1. 1.Facoltà di ArchitetturaUniversità di NapoliNapoliItaly
  2. 2.Department of Electrical EngineeringWinnipegCanada
  3. 3.Faculté de MédecineUniversité Aix-Marseille IIMarseilleFrance

Personalised recommendations