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From Artificial to Computational Intelligence

  • Christian Bierwirth
Part of the Operations Research Computer Science Interfaces Series book series (ORCS, volume 11)

Abstract

Traditional Artificial Intelligence (AI) claims the methodology of rule-based systems to be one of its leading programming paradigms1. Rule-based systems, also known as production systems, have been developed in the late sixties in order to provide a flexible representation of knowledge. They take advantage of deductive logic which is handled efficiently by symbolic data processing. Rule-based systems mainly became popular for the construction of Expert Systems which had a rapid spread at that time. In the seventies rule-based approaches run into their boundaries. The construction of decision making systems according to principles of human intelligence failed because of a lack of an adequate mechanism to extend available knowledge. A remedy was assumed from the incorporation of numerical knowledge representations. Some of the ideas developed at that time aimed at modeling processes of inference and learning on a computational basis. To stand out from the traditional symbolic AI, these approaches are captured by the modern term Computational Intelligence (CI).

Keywords

Artificial Neural Network Membership Function Fuzzy Logic Fuzzy Rule Evolutionary Computation 
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 2000

Authors and Affiliations

  • Christian Bierwirth
    • 1
  1. 1.University of BremenGermany

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