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Treatment of Vague Information in the Development of a Risk Evaluation System — Application to Seismic Risk Analysis

  • W. M. Dong
  • H. C. Shah
  • A. C. Boissonnade
Conference paper

Abstract

The development of an expert system involves processing many different types of information; some specific and some vague. Experts do have judgment and heuristic rules based upon their broad knowledge and experience. This judgmental knowledge often involves linguistic descriptors such as “serious,” “possible,” “important,” and so on. The way experts handle such information and reach assessment is also ill-structured and impossible to model using conventional mathematical means. This paper describes the use of fuzzy sets theory to incorporate vague information in the formulation of rules and the querying process when perfect matching no longer exists and contradictory statements are present. Two techniques, fuzzy system identification and the vertex method, are discussed in the treatment of information to obtain the risk functions. The models will be illustrated by means of examples derived from a seismic risk study.

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References

  1. Boissonnade, A. C., Dong, W. M., Shah, H. C. and Wong, F. C., (1985), “Identif ication of Fuzzy Systems in Civil Engineering,” Proceedings of International Symposium on Fuzzy Mathematics in Earthquake Researches, Beijing, China, pp. 48–71.Google Scholar
  2. Buchanan, B. G. and Shortliffe, E. H., (1984), Rule-Based Expert Systems; the MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-Wesley.Google Scholar
  3. Campbell, A., Hollister, N., Duda, V. F. and Hart, P. E., “Recognition of a Hidden Mineral Deposit by an Artificial Intelligence Program,” Science, Vol. 217, No. 3, Sept. (1982).Google Scholar
  4. Mamdani, E. H., Assilian, S., (1975), “An Experiment in Linguistic Synthes is with a Fuzzy Logic Controller,” Int. J. Man-Machine Studies, 7 .Google Scholar
  5. Pedrycz, W., (1984), “An Identification Algorithm in Fuzzy Relational Systems,” J. Fuzzy Sets and Systems 13, pp. 153–167.CrossRefzbMATHMathSciNetGoogle Scholar
  6. Dong, W. M. and Shah, H. C., (1985), “Vertex Method For Computing Function of Fuzzy Variables.” Submitted to Journ al of Fuzzy Sets and Systems .Google Scholar
  7. Duda, R., Hart, P., and Nilsson, N.,(1976), “Sub jective Bayesi an Methods for Rule-based Inference Systems.” Proceedings of National Computer Conference, pp. 1075–1082.Google Scholar
  8. Miyasato, G. H., Dong, W. M., Levitt, R. E. and Boissonnade, A. C., (1985), “Implementation of a Knowledged-Based Seismic Risk Evaluation System on Microcomputers,” submitted to Journal of AI in Engineering.Google Scholar
  9. Nilsson, N., (1980), “Principles of Artificial Intelligence,” Tioga, Palo Alto, California.zbMATHGoogle Scholar
  10. Ogawa, H., Fu, K. S., Yao, J.T.P., (1984), “SPERIL II: An Expert System for Da m age Asses s men t of Existing Structures,” Report No. CEE-STR-84–11, Purdue University, IN.Google Scholar
  11. Pedrycz, W., (1984) , “An Identification Algorithm in Fuzzy Relational Systems,” J. Fuzzy Sets and Systems 13, pp. 153–167.CrossRefzbMATHMathSciNetGoogle Scholar
  12. Zadeh, L. A., (1964), “Fuzzy Sets,” J. Fuzzy Sets and Systems, Memo, EERL, No. 64–44, University of California, Berkeley.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • W. M. Dong
    • 1
  • H. C. Shah
    • 1
  • A. C. Boissonnade
    • 2
  1. 1.Stanford UniversityUSA
  2. 2.Stanford University and Jack R. Benjamin & Assoc.USA

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