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Artificial Intelligence, Expert Systems, and Productivity

  • Sally Yeates Sedelow
  • Walter A. SedelowJr.

Abstract

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.

Keywords

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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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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