Learning Similarity Metrics from Case Solution Similarity

  • Carlos Morell
  • Rafael Bello
  • Ricardo Grau
  • Yanet Rodríguez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)


Defining similarity metrics is one of the most important tasks when developing Case Based Reasoning (CBR) systems. The performance of the system heavily depends on the correct definition of its similarity metric. To reduce this sensitivity, similarity functions are parameterized with weights for features. Most approaches to learning feature weights assume CBR systems for classification tasks. In this paper we propose the use of similarity between case solutions as a heuristic to estimate similarity between case descriptions. This estimation is used to adjust weights for features. We present an experiment in the domain of Case Based Process Planning that shows the effectiveness of this approach.


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  1. 1.
    Dasarathy, B.V. (ed.): Nearest neighbor norms: NN Pattern ClassificationTechniques. IEEE Computer Society Press, Los Alamitos (1990)Google Scholar
  2. 2.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  3. 3.
    Aamod, A., Plaza, E.: Case-based reasoning: Foundational Issues. Methodological Variations, and System Approaches. AI Communications 7(1) (1994)Google Scholar
  4. 4.
    Wettschereck, D., Aha, D.W.: Weighting features. In: Proceeding of the 1st International Conference on Case-Based Reasoning. Springer, Heidelberg (1995)Google Scholar
  5. 5.
    Hanks, S., Weld, D.S.: A domain-independent algorithm for Plan adaptation. Journal of Artificial Intelligence Research 2, 319–360 (1995)Google Scholar
  6. 6.
    Wilke, W., Bergmann, R.: Techniques and knowledge used for adaptation during case-based problem solving. In: 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (1997)Google Scholar
  7. 7.
    Leake, D.: Case-based reasoning: Experiences, Lessons and Future Direction. AAAI Press/ MIT Press (1996)Google Scholar
  8. 8.
    Leake, D.B.: Learning Adaptation Strategies by Introspective Reasoning about Memory Search. In: Leake, D. (ed.) Proceedings AAAI 1993 Workshop on Case- Based Reasoning, pp. 57–63. AAAI Press, Menlo Park (1993)Google Scholar
  9. 9.
    Smyth, B., Keane, M.T.: Retrieving adaptable cases. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 209–220. Springer, Heidelberg (1994)Google Scholar
  10. 10.
    Bergmann, R., Wilke, W.: Towards a new formal model of transformational adaptation in Case-based reasoning. In: Prade, H. (ed.) Proceedings of European Conference on Artificial Intelligence ECAI 1998. John Wiley and Sons, Chichester (1998)Google Scholar
  11. 11.
    Smyth, B., et al.: Chapter 9. In: Leake, D. (ed.) Case-based reasoning: Experiences, Lessons and Future Direction. AAAI Press/ MIT Press 199Google Scholar
  12. 12.
    Mille, A., et al.: A unifying framework for Adaptation in case-based reasoning. In: Wahlster, W. (ed.) ECAI 1996 12th European Conference on Artificial intelligence. John Wiley & Sons, Chichester (1996)Google Scholar
  13. 13.
    Smyth, B., Keane, M.T.: Experiments on adaptation-guided retrieval in case-based design. In: Proceedings of the First International Conference on Case-Based Reasoning, Portugal, pp. 313–324 (1995)Google Scholar
  14. 14.
    Goel, A.: Integration of Case-Based Reasoning and Model-Based reasoning for adaptive design problem solving. PhD thesis. Ohio State University (1989)Google Scholar
  15. 15.
    Brand, M., et al.: A model-based approach to the construction of adaptative case-based planning systems. In: Proceedings of the Case Based Reasoning Workshop, Florida, USA (1989)Google Scholar
  16. 16.
    Fox, S., Leake, D.: Modeling case-based planning for repairing reasoning failures. In: Proceedings of the 1995 AAAI Spring Symposium on Representing Mental States and Mechanisms. AAAI Press, Menlo Park (1995) Technical report WS-95-05Google Scholar
  17. 17.
    Ihrig, L., Kambhampati, S.: An explanation-based approach to improve retrieval in case-based planning. In: Current Trends in AI Planning: EWSP 1995. IOS Press, Amsterdam (1995)Google Scholar
  18. 18.
    Bergmann, R., Richter, M.M., Schmitt, S., Stahl, A., Vollrath, I.: Utility-Oriented Matching: A New Research Direction for Case-Based Reasoning. In: 9th German Workshop on Case-Based Reasoning (GWCBR 2001) (2001)Google Scholar
  19. 19.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707–710 (1966)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carlos Morell
    • 1
  • Rafael Bello
    • 1
  • Ricardo Grau
    • 1
  • Yanet Rodríguez
    • 1
  1. 1.Computer Sciences Department.Universidad Central de Las VillasCuba

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