Knowledge representation in expert systems in a linguistic form

ABSTRACT

A good expert system needs to be as close as possible to the knowledge and data manipulation of the expert himself. Often experts use linguistic instead of numerical values. When input data are mostly qualitative and are based on subjective knowledge of experts, the Fuzzy Set Theory is a solid mathematical model to represent and handle these data. APL arrays, scalars, vectors, matrices, operands etc. provide powerful means for implementing fuzzy sets. In this paper we will show the capabilities of APL to represent knowledge in a linguistic form.

REFERENCES

• 1: Ian Graham , Peter Llewelyn Jones, Expert systems: knowledge, uncertainty, and decision, Chapman & Hall, Ltd., London, UK, 1988
• 2: Ian Graham, Fuzzy logic in commercial expert system-results and prospects, Fuzzy Sets and Systems, v.40 n.3, p.451-472, April 15, 1991 [doi>10.1016/0165-0114(91)90172-M]
• 3: Constantin Virgil Negoita, Expert systems and fuzzy systems, Benjamin-Cummings Publishing Co., Inc., Redwood City, CA, 1984
• 4: E.E. Kerre, Introduction to the Basic Principles of Fuzzy Set Theo~ and some of its Applications (Communication & Cognition, Gent, 1991).
• 5: E.E. Kerre, Vage vetzamelingen met toepassingen in de informatica (Seminarie veer wiskundige analyse, Universiteit Gent).
• 6: George J. Klir , Tina A. Folger, Fuzzy sets, uncertainty, and information, Prentice-Hall, Inc., Upper Saddle River, NJ, 1987
• 7: H.J. Zimmerman, L.A. Zadeh and B.R.Gaines, Fuzzy Sets and Decision Analysis North-Holland, Amsterdam, 1984).
• 8: J.L.F. de Kerr, Vage Verzamelingen, Het lngenieursblad, nr 19/1974, 43e jaargang.
• 9: A.J. Rose, B.A. Schick, APL In Practice (John Wiley & Sons, Inc., New-York, 1980).
• 10: D Stiers , M J Goovaerts , J De Keef, APL, the language and its actuarial applications, Elsevier North-Holland, Inc., New York, NY, 1986
• 11: D.J.W. Stiers, F.C.M. Broeckx, M.J. Goovaerts, APL een taal veer rekenkundige toepassingen (Kluwer, Deventer, 1988).
• 12: Leonard Gilman , Allen J. Rose, APL: An Interactive Approach, John Wiley & Sons, Inc., New York, NY, 1976
• 13: J.L.F. De Kerr, VAX APL - An Introduction (Training Seminar, 24 march 1992).
• 14: J. Brown, TryAPL2 for free, Version 1.05 (IBM Corporation, oct 1990).
• 15: Gary W. Dickson , James C. Wetherbe, The management of information systems, McGraw-Hill, Inc., New York, NY, 1985
• 16: H. de Swaan Arons, P. van Lith, Expert Systemen (Academic Service, Den Haag, 1984).
• 17: Earl Cox, Applications of Fuzzy System Models, (AI expert, oct 1992)
• 18: Joris De Meyer, Vage Logica in Commerci~le Expertsystemen, enkele toepassingen in APL (UIA, Antwerpen 1992)
• 19: R. Bellman and Zadeh, Decision making in a fuzzy environment, (Management Sci, 1970)
• 20: R.N. Andries : Class : A structured scenario Analysis Tool for planning ISDN Services : Electrical communication, Vol. 64, no. 1, 1990 pp. 107.
• 21: John Fox, Knowledge, decision making, and uncertainty, Artificial intelligence and statistics, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1986

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representations (procedural and rule-based)

Additional Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.2 Language Classifications

          Nouns: APL

  F. Theory of Computation
  F.4 MATHEMATICAL LOGIC AND FORMAL LANGUAGES

General Terms:
Algorithms, Languages, Theory