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Automating Case Selection in the Construction of a Case Library

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Research and Development in Intelligent Systems XVI

Abstract

An approach to case selection in the construction of a case library is presented in which the most useful case to be added to the library is identified by evaluation of the additional coverage provided by candidate cases. Cases that can be solved by the addition of a candidate case to the library are discovered in the approach by reversing the direction of case-based reasoning. The computational effort required in the evaluation of candidate cases can be reduced by focusing the search on a specified region of the problem space. The approach has been implemented in CaseMaker, an intelligent case-acquisition tool designed to support the authoring process in a case-based reasoner for estimation tasks.

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References

  1. Leake D, Wilson D. Categorizing case-base maintenance: dimensions and directions. In: Smyth B, Cunningham P (eds) Advances in Case-Based Reasoning, Springer-Verlag, Berlin-Heidelberg, 1998, pp 196–207 (Lecture notes in artificial intelligence no. 1488)

    Chapter  Google Scholar 

  2. Smyth B, McKenna E. Modelling the competence of case-bases. In: Smyth B, Cunningham P (eds) Advances in Case-Based Reasoning, Springer-Verlag, Berlin-Heidelberg, 1998, pp 208–220 (Lecture notes in artificial intelligence no. 1488)

    Chapter  Google Scholar 

  3. Watson I . Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, San Francisco, 1997

    MATH  Google Scholar 

  4. Aha D, Breslow L. Refining conversational case libraries. In: Leake D, Plaza E (eds) Case-Based Reasoning Research and Development. Springer-Verlag, Berlin-Heidelberg, 1997, pp 267–278 (Lecture notes in artificial intelligence no. 1266)

    Chapter  Google Scholar 

  5. Zhu J, Yang Q. Remembering to add: competence-preserving case-addition policies for case-base maintenance. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, Stockholm, 1999, pp 234–239

    Google Scholar 

  6. Racine K, Yang Q. Maintaining unstructured case bases. In: Leake D, Plaza E (eds) Case-Based Reasoning Research and Development. Springer-Verlag, Berlin-Heidelberg, 1997, pp 553–564 (Lecture notes in artificial intelligence no. 1266)

    Chapter  Google Scholar 

  7. McSherry D. An adaptation heuristic for case-based estimation. In: Smyth B, Cunningham P (eds) Advances in Case-Based Reasoning, Springer-Verlag, Berlin-Heidelberg, 1998, pp 184–195 (Lecture notes in artificial intelligence no. 1488)

    Chapter  Google Scholar 

  8. McSherry D. Relaxing the similarity criteria in adaptation knowledge discovery. Proceedings of the IJCAI-99 Workshop on Automating the Construction of Case-Based Reasoners, Stockholm, 1999, pp 56–61

    Google Scholar 

  9. McSherry D. Demand-driven discovery of adaptation knowledge. Poceedings of the Sixteenth International Joint Conference on Artificial Intelligence, Stockholm, 1999, pp 222–227

    Google Scholar 

  10. McSherry D. Differentiation by case-based reasoning. Pre-Proceedings of the Tenth Irish Conference on Artificial Intelligence and Cognitive Science, Cork, 1999, pp 150–156

    Google Scholar 

  11. Smyth B, Keane M. Remembering to forget: a competence-preserving case deletion policy for CBR systems. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, 1995, pp 377–382

    Google Scholar 

  12. Hanney K, Keane M. Learning adaptation rules from a case-base. In: Smith I, Faltings B (eds) Case-Based Reasoning Research and Development. Springer-Verlag, Berlin- Heidelberg, 1996, pp 178–192 (Lecture notes in artificial intelligence no. 1168)

    Google Scholar 

  13. Smyth B, Keane M. Adaptation-guided retrieval: questioning the similarity assumption in reasoning. Artif Intell 1998; 102: 249–293

    Article  MATH  Google Scholar 

  14. Frawley W, Piatetsky-Shapiro G, Matheus C. Knowledge discovery in databases: an overview. In: Piatetsky-Shapiro G, Frawley W (eds) Knowledge Discovery in Databases, AAAI Press, Menlo Park, 1991, pp 1–27

    Google Scholar 

  15. Smyth P, Goodman R. Rule induction using information theory. In: Piatetsky-Shapiro G, Frawley W (eds) Knowledge Discovery in Databases, AAAI Press, Menlo Park, 1991, pp 159–176

    Google Scholar 

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© 2000 Springer-Verlag London Limited

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McSherry, D. (2000). Automating Case Selection in the Construction of a Case Library. In: Bramer, M., Macintosh, A., Coenen, F. (eds) Research and Development in Intelligent Systems XVI. Springer, London. https://doi.org/10.1007/978-1-4471-0745-3_11

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  • DOI: https://doi.org/10.1007/978-1-4471-0745-3_11

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-231-0

  • Online ISBN: 978-1-4471-0745-3

  • eBook Packages: Springer Book Archive

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