Functional Properties and Concept Formation

  • J. Daniel Easterlin
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 12)


Previous research in Machine Learning suggests that the process of concept formation can be divided into three distinct components. The first of these — aggregation — involves grouping instances of experience into collections. The second component — characterization — involves generating a description of the instances in the aggregate. The final process — utilization — consists of making use of the resulting description. These components are examined in more detail in this paper and the derivation of functional properties of a real—world environment is discussed within this framework.


Machine Learn Functional Property Learning System Concept Formation World State 
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

© Kluwer Academic Publishers 1986

Authors and Affiliations

  • J. Daniel Easterlin
    • 1
  1. 1.Irvine Computational Intelligence ProjectDepartment of Information and Computer Science, University of CaliforniaIrvineUSA

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