Artificial Intelligence, Expert Systems, and Productivity

  • Sally Yeates Sedelow
  • Walter A. SedelowJr.


This chapter discusses the terms Artificial Intelligence and Expert Systems before turning to a more general consideration of types of knowledge and their representation. Technology is leading us toward making knowledge algorithmic (procedural and ruleful). The implications of this development for productivity, especially as affected by resulting workforce attitudes, are likely to be monumental. Notably responsive will be not only blue-collar workers but also white-collar workers, whose stock-in-trade is symbolic manipulation. As technology becomes ever more facilitative of machine-based symbolic analysis and communication, traditional workforce roles, which hitherto were not threatened, inevitably will be affected. Nonetheless, in the near term, symbol systems (W. Sedelow & S. Sedelow, 1979, 1983) and the knowledge they represent pose formidable research and development challenges to technology-based productivity. Verbal symbol systems, with their ambiguities and vaguenesses, are especially difficult to manage in a multidomain-specific way; but there are promising approaches to these problems. Attention needs to be paid not only to such problem resolution but to the interfitting of Expert Systems, and Artificial Intelligence more generally, with Robotics.


Expert System Knowledge Representation Artificial Intelligence Research Parallel Distribute Processing Case Frame 
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. Ashby, W. R. (1952). Design for a brain. New York: Chapman & Hall.Google Scholar
  2. Bryan, R. M. (1973). Abstract thesauri and graph theory applications to thesaurus research. In S. Y. Sedelow (Ed.), Automated language analysis (pp. 1972–1973 ). Lawrence: University of Kanses, Departments of Computer Science and Linguistics; also Defense Documentation Center, #AD 774–692.Google Scholar
  3. Feigenbaum, E. A. (1983). Knowledge engineering. The applied side. In J. E. Hayes & D. Michie (Eds.), Intelligent systems: The unprecedented opportunity. New York: Halstead Press.Google Scholar
  4. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan.Google Scholar
  5. Kuhn, T. S. (1970). The structure of scientific revolutions (2nd ed., rev.). Chicago: University of Chicago.Google Scholar
  6. Marr, D. (1982). Vision. San Francisco: W. H. Freeman.Google Scholar
  7. Miller, J. G. (1977). Living systems. New York: McGraw-Hill.Google Scholar
  8. Pawlak, Z. (1981a). Classification of objects by means of attributes (Institute of Computer Science #429). Warsaw: Polish Academy of Sciences.Google Scholar
  9. Pawlak, Z. (1981b). Technical report #435, Institute of Computer Science #435. Warsaw: Polish Academy of Sciences.Google Scholar
  10. Roget’s International Thesaurus (3rd ed.). (1962). New York: Thomas Y. Crowell.Google Scholar
  11. Rosenfield, I. (1984). Seeing through the brain. New York Review of Books (year, October 11 ), pp. 53–56.Google Scholar
  12. Sedelow, S. Y. (1969). Prefix. In S. Y. Sedelow (Ed.), Automated language analysis, 1968–1969. Chapel Hill: University of North Carolina, Departments of English and Computer & Information Science; also DDC #AD 691–451.Google Scholar
  13. Sedelow, S. Y. (1985). Computational literary thematic analysis: The possibility of a general solution. In C. Parkhurst (Ed.), Proceedings of the 48th ASIS annual meeting 22 (pp. 359–362).Google Scholar
  14. Sedelow, S. Y., & Sedelow, W. A., Jr. (1969). Categories and procedures for content analysis in the humanities. In G. Gerbner et al. (Eds.), The analysis of communication content (pp. 487–499 ). New York: John Wiley & Sons, Inc.Google Scholar
  15. Sedelow, S. Y., & Sedelow, W. A., Jr. (1986a). The lexicon in the background. Computers and Translation, 1(2), 73–81.CrossRefGoogle Scholar
  16. Sedelow, S. Y., & Sedelow, W. A., Jr. (1986b). Thesaural knowledge representation. Proceedings of the Second Annual Conference of the UW (Canada) Centre for the New Oxford English Dictionary, Advances in Lexicology (pp. 29–43.Google Scholar
  17. Sedelow, W. A., Jr. (1968). History as language. Computer Studies in the Humanities and Verbal Behavior, 1(4), 183–190.Google Scholar
  18. Sedelow, W. A., Jr. (1976). From faceted to integrated human/computer systems theory. 8th Southeastern Symposium on System Theory Proceedings (pp. 283–288 ). New York: IEEE, Inc.Google Scholar
  19. Sedelow, W. A., Jr. (1978). The mechanomorphic man and the anthropomorphic machine. Abstracts for 1st International Conference on Creatures of Legendry. Omaha: University of Nebraska.Google Scholar
  20. Sedelow, W. A., Jr. (1980). Algorithm and empire: The new imperialism as an abstract machine theory instantiation. Omaha: University of Nebraska, European Studies Conference.Google Scholar
  21. Sedelow, W. A., Jr. (1985). Semantics for humanities applications: Context and significance of semantic “Stores.” In C. Parkhurst (Ed.), Proceedings of the 48th ASIS Annual Meeting (pp. 363–366 ), 22.Google Scholar
  22. Sedelow, W. A., Jr., & Sedelow, S. Y. (1978). Formalized historiography, the structure of scientific and literary texts: Part I. Some issues posed by computational methodology. Journal of the History of the Behavioral Sciences, 14, 247–263.CrossRefGoogle Scholar
  23. Sedelow, W. A., Jr., & Sedelow, S. Y. (1979). The history of science as discourse. Journal of the History of the Behavioral Sciences, 15, 63–72.CrossRefGoogle Scholar
  24. Sedelow, W. A., Jr., & Sedelow, S. Y. (eds.). (1983). Formalization in literary and discourse analysis…. The Hague: Mouton. and (1979). Formal methods in language research. The Hague: Mouton.Google Scholar
  25. Sedelow, W. A., Jr., & Sedelow, S. Y. (in press). Semantic space. Computers and Translation, 2, 235–246.Google Scholar
  26. Winograd, T., & Flores, F. (1987). Understanding computers and cognition. Ablex. For extensive discussion of this book, see (1987) Artificial Intelligence, 31(2), pp. 213–262.Google Scholar
  27. Winston, P. (1984). Artificial intelligence ( 2nd ed. ). Reading, MA: Addison-Wesley.Google Scholar

Copyright information

© Springer Science+Business Media New York 1988

Authors and Affiliations

  • Sally Yeates Sedelow
    • 1
  • Walter A. SedelowJr.
    • 1
  1. 1.University of Arkansas at Little Rock and University of Arkansas Graduate Institute of TechnologyUSA

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