Knowledge Representation and Inference in Knowledge Based Systems (Expert Systems)

  • Piero P. Bonissone
Conference paper
Part of the Lecture Notes in Engineering book series (LNENG, volume 53)


Knowledge Based Systems (KBS) are computer programs in which knowledge and control arc explicitly separated. In first generation KBS, the reasoning is usually monotonic and the control is procedural. Second generation KBS usually exhibit nonmonotonic reasoning, declarative control, and more sophisticated representations of uncertainty. We will focus on the first generation KBS and analyze their typical architecture, composed of a Knowledge Base (KB), a Working Memory (WM), and an Inference Engine (IE). The KB describes the domain knowledge; the WM describes a problem instance; the IE determines the applicability of different subsets of the KB to the current problem. The selection of a specific knowledge representation paradigm, used to build the KB, implicitly determines the selection of the inference mechanism to be used. We will briefly discuss Predicate Calculus, which uses unification and resolution, Frames, which use inheritance, and Production Rules, which use rule chaining.


Expert System Turing Machine Working Memory Production Rule Inference Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    William J. Clanccy. Classification Problem Solving. In Proceedings Fourth National Conference on Artificial Intelligence, pages 49–55. AAAI, August 1984.Google Scholar
  2. [2]
    E.H. Shortliffe and B. Buchanan. A Model of Inexact Reasoning in Medicine. Mathematical Biosciences, 23:351–379, 1975.CrossRefMathSciNetGoogle Scholar
  3. [3]
    B.G. Buchanan and E.H. Shortliffe. Rule-Based Expert Systems. Addison-Wesley, Reading, Massachusetts, 1984.Google Scholar
  4. [4]
    Wendy B. Rauch-Hindin. Artificial Intelligence in Business, Science, and Industry. Prentice Hall, 1985.Google Scholar
  5. [5]
    R.O. Duda, J. Gaschnig, P.E. Hart, K. Konolige, Reboh R., P. Barrett, and J. Slocum. Development of a computer-based consultant for mineral exploration. Sri project 5821 final report and sri project 6415 annual report, SRI International, Artificial Intelligence Center, Menlo Park, California, 1978.Google Scholar
  6. [6]
    P. P. Bonissone. Delta: An expert system to troubleshoot diesel electric locomotives. In Proceeding of ACM 83, pages 44–45. ACM, New York, 1983.CrossRefGoogle Scholar
  7. [7]
    P. P. Bonissone and H. E. Johnson. Expert system for diesel electric locomotive repair. Human Systems Management, 4:255–262, 1984.Google Scholar
  8. [8]
    Piero P. Bonissone and Nancy C Wood. Plausible Reasoning in Dynamic Classification Problems. In Proceedings of the Validation and Testing of Knowledge-Based Systems Workshop. AAAI, August 1988.Google Scholar
  9. [9]
    Intellicorp. KEE Software Development System User’s Manual, 1986. Version 3.0.Google Scholar
  10. [10]
    Inference. ART Reference Manual, 1987. Version 3.0.Google Scholar
  11. [11]
    Piero P. Bonissone, Stephen Gans, and Keith S. Decker. RUM: A Layered Architecture for Reasoning with Uncertainty. In Proceedings 10th International Joint Conference on Artificial Intelligence, pages 891–898. AAAI, August 1987.Google Scholar
  12. [12]
    W.B. Gevarter. Expert Systems: limited but powerful. IEEE Spectrum, 20(8):39–45, 1983.Google Scholar
  13. [13]
    N.J. Nilsson. Problem-Solving Methods in Artificial Intelligence. McGraw-Hill, 1971.Google Scholar
  14. [14]
    Chin-Liang Chang and Richard Char-Tung Lee. Symbolic Logic and Mechnaical Theorem Proving. Acadermic Press, 1973.Google Scholar
  15. [15]
    W.J. Rapaport. Predicate Logic. In Stuart Shapiro, Editor, Encyclopedia of Artificial Intelligence, pages 538–544. John Wiley and Sons Co., New York, 1987.Google Scholar
  16. [16]
    M. Minsky. A Framework for Representing Knowledge. In P. Winston, Editor, The Psychology of Computer Vision, pages 211–277. McGraw-Hill, New York, 1975.Google Scholar
  17. [17]
    R.Schank and R.P. Abelson. Scripts, Plans, Goals, and Understanding. Erlbaum, Hillsdale, N.J., 1977.MATHGoogle Scholar
  18. [18]
    F.C. Bartlett. Remembering: A Study in Experimental And Social Psychology. The University Press, Cambridge, 1932. Revised 1961.Google Scholar
  19. [19]
    J. Moore and A. Newell. How can Merlin Understand? In L.W. Gregg, Editor, Knowledge and Cognition, pages 201–252. Erlbaum, 1973.Google Scholar
  20. [20]
    M.R. Quillian. Semantic Memory. In M. Minsky, Editor, Semantic Information Processing. M.I.T. Press, Cambridge, MA., 1968.Google Scholar
  21. [21]
    A.S. Maida. Frame Theory. In Stuart Shapiro, Editor, Encyclopedia of Artificial Intelligence, pages 302–312. John Wiley and Sons Co., New York, 1987.Google Scholar
  22. [22]
    A.A. Markov. The Theory of Algorithms (Russian). Trudy Mathematicheskogo Instituta imeni V.A. Steklova, 38:176–189, 1951. Translated from the Russian by J.J. Schorr-Kon, U.S. Department of Commerce, Office of Technical Services, number OTS 60-51085.Google Scholar
  23. [23]
    H.R. Lewis and C. H. Papadimitriou. Elements of the Theory of Computation. Prentice Hall, 1981.MATHGoogle Scholar
  24. [24]
    C. Forgy and J. McDermott. OPS, a domain-independent production system language. In Proc. Fifth Intern. Conf. on Artificial Intelligence, pages 933–939, 1977.Google Scholar
  25. [25]
    Lee Brownston, Robert, Elaine Kant, and Nancy Martin. Programming Expert Systems in OPS5. Addison-Wesley, Reading, Massachusetts, first edition, 1985.Google Scholar
  26. [26]
    F. Hayes-Roth, D. Waterman, and D.Lenat (Eds.). Builiding Expert Systems. Addison-Wesley, 1983.Google Scholar
  27. [27]
    J. McDermott and C. Forgy. Production System Conflict Resolution Strategies. In D. Waterman and F. Hayes-Roth, Editors, Pattern-Directed Inference Systems, pages 177–199. Academic Press, New York, 1978.Google Scholar

Copyright information

© Springer-Verlag Berlin, Heidelberg 1989

Authors and Affiliations

  • Piero P. Bonissone
    • 1
  1. 1.General Electric Corporate Research and DevelopmentArtificial Intelligence ProgramSchenectadyUSA

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