The Experiential Knowledge Base as a Cognitive Prosthesis

  • Panos A. Ligomenides
Part of the Management and Information Systems book series (LISS)


Growing demands in applications, rapid advances in computer technology, and the continuing impact of the information explosion have caused, during the last decade, substantial progress in the technology of complex data management systems. A parallel trend towards using knowledge-based systems in a support role for decision making has also been on the rise in recent years. Expert knowledge systems that use encoded expertise are being designed to solve diagnostic, classification, and planning problems, in ways that resemble those of human experts. In addition to their use by decision makers as consultation resources for policy making, expert knowledge systems may also be used so that managers may intelligently exploit the vast databases of the advanced management information systems, by having ready access to expertise about finances, planning, and company policies.


Decision Maker Temporal Logic Experiential Knowledge Knowledge Model Behavioral Modality 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. F. Allen, Toward a general theory of action and time, Artif. Intell. 23, 123–154 (1984).CrossRefGoogle Scholar
  2. 2.
    P. Bonissone, A fuzzy sets based linguistic approach: Theory and applications, in Approximate Reasoning in Decision Analysis, M. Gupta and E. Sanchez (Eds.), North-Holland, Amsterdam, 1982.Google Scholar
  3. 3.
    E. Charniak, A common representation for problem-solving and language-comprehension information, Artif. Intell. 16, 225–255.Google Scholar
  4. 4.
    A. Goldman, A Theory of Human Action, Princeton University Press, Princeton, New Jersey, 1970.Google Scholar
  5. 5.
    E. A. Feigenbaum et al., On generality and problem-solving: A case study involving the DEN-DRAL program, in Machine Intelligence, Meitzer and Michie (Eds), Elsevier, New York, 1971, Vol. 6, pp. 165–190.Google Scholar
  6. 6.
    C. J. Fillmore, The case for case, in Universals in Linguistic Theory, Bach and Harms (Eds.), Holt, Rinehart, and Winston, New York, 1968.Google Scholar
  7. 7.
    N. V. Findler (Ed.), Associative Networks, Academic, New York, 1979.Google Scholar
  8. 8.
    R. Jackendoff, Toward an explanatory semantic representation, Linguistic Inquiry 7 (1), 89–150 (1976).Google Scholar
  9. 9.
    P. A. Ligomenides, An engineering-cybernetic model for policy analysis and implementation, Int. J. PAIS 6 (3), 273–284 (1982).Google Scholar
  10. 10.
    P. A. Ligomenides, Models for information quality enhancement, Proc. IEEE Workshop on Language for Automation, Chicago, November 7–9, 1983.Google Scholar
  11. 11.
    P. A. Ligomenides, Command decomposition as a decision making problem, in Management and Office Information Systems, S. K. Chang (Ed.), Plenum Press, New York, 1984, pp. 401–414.Google Scholar
  12. 12.
    P. A. Ligomenides, Specifications of an experiential data base for decision making, Proc. IEEE Conf. on Trends and Applic., NBS, May 2–24, 1984.Google Scholar
  13. 13.
    P. A. Ligomenides, Organization and operation of an experiential knowledge base, Proc. IEEE Workshop on Lang, for Automation, Mallorca, Spain, June 28–29, 1985.Google Scholar
  14. 14.
    P. A. Ligomenides, A cellular experiential memory, Proc. Int’l Conf. Sys. Man and Cybern., Tucson, Arizona, November 12–15, 1985.Google Scholar
  15. 15.
    P. A. Ligomenides, Prediction and stability of behavioral signatures produced in the E*KB, Proc. IEEE Symp. on AI in Eng., Washington, DC, October 21–23, 1985.Google Scholar
  16. 16.
    P. A. Ligomenides, Perceptual modeling and recognition of behavioral modalities, Technical Report CRL/EE TR85/3, E.E. Dept., Univ. of Maryland, November 1, 1985. Also, to be published.Google Scholar
  17. 17.
    P. A. Ligomenides, Notions and dynamics of information, J. Inf. Sci. 10 (4), 149–158, (1986).CrossRefGoogle Scholar
  18. 18.
    J. McCarthy and P.J. Hayes, Some philosophical problems from the standpoint of artifical intelligence, in Machine Intelligence 4, B. Meitzer and D. Michie (Eds.), Edinburgh U. Press, Edinburgh, 1969.Google Scholar
  19. 19.
    D. McDermott, A temporal logic for reasoning about processes and plans, Cognitive Sci. 6 (2), (1982).Google Scholar
  20. 20.
    A. P. D. Mourelatos, Events, processes and states, Linguistics Phil. 2, 415–534.Google Scholar
  21. 21.
    J. Moses, Symbolic integration: The stormy decade, Commun. ACM 8, 548–560 (1971).CrossRefGoogle Scholar
  22. 22.
    A. Newell and H. A. Simon, Human Problem Solving, Prentice-Hall, Englewood Cliffs, New Jersey, 1972.Google Scholar
  23. 23.
    Ju. A. Schreider, Equality, Resemblance and Order, Mir Publishers, Moscow, 1975 (transí. M. Greendlinger).Google Scholar
  24. 24.
    S. J. Segal (Ed.), Imagery: Current Cognitive Approaches, Academic, New York, 1971.Google Scholar
  25. 25.
    E. H. Shortliffe and B. G. Buchanan, A model of inexact reasoning in medicine, Math. Biosci. 23, 351–379 (1975).CrossRefGoogle Scholar
  26. 26.
    E. H. Shortliffe, Computer-Based Medical Consultations: MYCIN, North-Holland, Amsterdam, 1976.Google Scholar
  27. 27.
    L. A. Zadeh, Quantitative fuzzy semantics, Inform. Sci. 3, 159–176 (1971).CrossRefGoogle Scholar
  28. 28.
    L. A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Syst. Man Cybern. SMC-3(1), 28–44 (1973). 452CrossRefGoogle Scholar
  29. 29.
    L. A. Zadeh, A Theory of Commonsense Knowledge, Mem. No. UCB/ERL M83/26, E.E.C.S. Dept., Univ. of California, Berkeley,, Cal. 17 April, 1983.Google Scholar

Copyright information

© Plenum Press, New York 1986

Authors and Affiliations

  • Panos A. Ligomenides
    • 1
  1. 1.Electrical Engineering DepartmentUniversity of MarylandCollege ParkUSA

Personalised recommendations