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International Conference on Knowledge Engineering and Knowledge Management

EKAW 1996: Advances in Knowledge Acquisition pp 48–65Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Authors and Affiliations

  1. Swedish Institute of Computer Science, Box 1263, 164 28, Kista, Sweden

    Klas Orsvärn

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  1. Klas Orsvärn
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Nigel Shadbolt Kieron O'Hara Guus Schreiber

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© 1996 Springer-Verlag Berlin Heidelberg

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Orsvärn, K. (1996). Principles for libraries of task decomposition methods — Conclusions from a case-study. In: Shadbolt, N., O'Hara, K., Schreiber, G. (eds) Advances in Knowledge Acquisition. EKAW 1996. Lecture Notes in Computer Science, vol 1076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61273-4_4

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  • DOI: https://doi.org/10.1007/3-540-61273-4_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61273-5

  • Online ISBN: 978-3-540-68391-9

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