Overview of Intelligent Systems

  • Larry R. Medsker


Several intelligent computing technologies are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. This chapter presents the fundamentals of individual intelligent technologies that will be important for understanding their integration.


Genetic Algorithm Expert System Decision Support System Fuzzy System Intelligent System 
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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References for Further Reading

  1. Allen, B. P. (1994), “Case-based reasoning: business applications,” Communications of the ACM, vol 37, no. 3, pp. 40–42.CrossRefGoogle Scholar
  2. Allman, W. (1989), Apprentices of Wonder, Bantam Books, New York.Google Scholar
  3. Beale, R., and Jackson, T. (1990), Neural Computing, Adam Hilger, Bristol, England.zbMATHGoogle Scholar
  4. Buta, P. (1994), “Mining for-financial knowledge with CBR,” AI Expert, vol 9, no. 2, pp. 34–41.Google Scholar
  5. Caudill, M, and Butler, C. (1990), Naturally Intelligent Systems, MIT Press, Cambridge, MA.Google Scholar
  6. Gallant, S. I. (1993), Neural Network Learning and Expert Systems, MIT Press, Cambridge, MA.zbMATHGoogle Scholar
  7. Goldberg, D. E. (1994), “Genetic and evolutionary algorithms come of age,” Communications of the ACM, vol 37, no. 3, pp. 113–119.CrossRefGoogle Scholar
  8. Hedberg, S. (1994), “Emerging genetic algorithms,” AI Expert, vol 9, no. 9, pp. 24–29.Google Scholar
  9. Lawton, G. (1992), “Genetic algorithms for scheduling optimization,” AI Expert, vol 7, no. 5, pp. 23–27.Google Scholar
  10. Medsker, L. and Liebowitz, J. (1994), Design and Development of Expert Systems and Neural Networks, Macmillan Publishing Company, New York.zbMATHCrossRefGoogle Scholar
  11. Munakata, T., and Jani, Y. (1994), “Fuzzy systems: an overview,” Communications of the ACM, vol 37, no. 3, pp. 69–76.Google Scholar
  12. Rumelhart, D. E., Widrow, B., and Lehr, M. A. (1994), “The basic ideas in neural networks,” Communications of the ACM, vol 37, no. 3, pp. 87–92.CrossRefGoogle Scholar
  13. Stottler, R. H. (1994), “CBR for cost and sales prediction,” AI Expert, vol 9, no. 8, pp. 24–33.Google Scholar
  14. Turban, E. (1992), Expert Systems and Applied Artificial Intelligence, Macmillan Publishing Company, New York.Google Scholar
  15. Zadeh, L. A. (1965), “Fuzzy sets,” Information and Control, vol. 8, pp. 338–353.MathSciNetzbMATHCrossRefGoogle Scholar
  16. Zadeh, L. A. (1988), “Fuzzy Logic,” Computer, vol 21, pp. 83–93.CrossRefGoogle Scholar
  17. Zadeh, L. A. (1994a), “Fuzzy logic, neural networks and soft computing,” Communications of the ACM, vol 37, no. 3, pp. 77–84.MathSciNetCrossRefGoogle Scholar
  18. Zadeh, L. A. (1994b), “Fuzzy logic and the calculi of fuzzy rules, fuzzy graphs, and fuzzy probabilities,” Proceedings of the World Congress on Neural Networks, vol I, San Diego, pp. 695–696.Google Scholar

Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Larry R. Medsker
    • 1
  1. 1.Department of Computer Science and Information SystemsThe American UniversityUSA

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