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Principles for libraries of task decomposition methods — Conclusions from a case-study

  • Klas Orsvärn
Theoretical and General Issues
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1076)

Abstract

Chandrasekaran and Steels proposed several years ago that libraries of reusable problem solving methods, for use in model-driven knowledge acquisition, should be organized as hierarchies of task decomposition methods, rather than as collections of complete methods. One of the most comprehensive examples to date is Benjamins' library of methods for diagnosis tasks. In a case-study of using Benjamins' library, to model a specific diagnosis application, the most suitable model generated by the library had to be modified in several ways, despite the fact that the application is relatively simple and mainstream. This caused significant difficulties, both in identifying the modification requirements, and in creating the necessary adaptations. This paper proposes a set of general principles which libraries of task decomposition methods can be evaluated against, in order to prevent unnecessary adaptations. The principles concern method correctness, specialization of selection criteria, and method generality.

Keywords

Transfer Task Task Feature Interpretation Model Discrimination Method Symptom Detection 
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-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Klas Orsvärn
    • 1
  1. 1.Swedish Institute of Computer ScienceKistaSweden

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