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Representing Knowledge for Case-Based Reasoning: The Rocade System

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1898))

Abstract

This paper presents the object-based knowledge representation system Rocade, that is aimed at the development of case-based reasoning (cbr) systems. cbr is studied by reference to the two levels defined by Newell: at the knowledge level, a general detailed model of the cbr process has been proposed. This model is intended to be implemented at the symbol level materialized by the Rocade system. This paper presents these two complementary levels and focuses on Rocade. The concepts and reasoning mechanisms of Rocade are described, as well as its architecture. Then, its architecture allowing different ways to use it is presented. Rocade is illustrated with examples of two cbr systems. The implementation of 2 CBR systems are used to illustrate the rocade system the functionalities of the rocade system

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References

  1. Aamodt, A. (1991). A Knowledge-Intensive, Integrated Approach to Problem Solving and Sustained Learning. Doctoral dissertation, Trondheim University, TrondHeim, Norway.

    Google Scholar 

  2. Aamodt, A. and Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(1):39–58.

    Google Scholar 

  3. Althoff, K.-D., Auriol, E., Barletta, R., and Manago, M. (1995a). A Review of Industrial Case-Based Reasoning Tools. AI Intelligence.

    Google Scholar 

  4. Althoff, K.-D., Auriol, E., Bergmann, R., Breen, S., Dittrich, S., Johnston, R., Manago, M., Traphoner, R., and Wess, S. (1995). Case-Based Reasoning for Decision Support and Diagnostic Problem Solving: The INRECA Approach. In Proc. of the 3rd Workshop of the German special Interest Group on CBR at the 3rd German Expert System Conference.

    Google Scholar 

  5. Armengol, E. and Plaza, E. (1994). A Knowledge Level Model of Case-Based Reasoning. In Richter, M.M., Wess, S., Althoff, K.-D., and Maurer, F., editors, First European Workshop on Case-Based Reasoning-EWCBR-93, pages 53–64, Kaiserslautern, Germany. LNAI, vol. 837, Springer, Berlin.

    Google Scholar 

  6. Bergmann, R. and Althoff, K.-D. (1998). Methodology for Building CBR Applications, chapter 12, pages 299–326. In [Lenz et al., 1], LNAI 1400. Springer.

    Google Scholar 

  7. Fikes, R. and Kehler, T. (1985). The Role of Frame-Based Representation in Reasoning. Communications of ACM, 28(9):904–920.

    Article  Google Scholar 

  8. Fuchs, B. (1997). Représentation des connaissances pour le raisonnement á partir de cas: le système ROCADE. Thèse d’université, Université Jean Monnet, Saint-Etienne, France.

    Google Scholar 

  9. Fuchs, B. and Mille, A. (1999). A Knowledge-Level Task Model of Adaptation in Case-Based Reasoning. In Branting, K., Althoff, K.-D., and Bergmann, R., editors, Third International Conference on Case-Based Reasoning-ICCBR-99, pages 118–131, Seeon,Germany. LNAI, Springer, Berlin.

    Google Scholar 

  10. Fuchs, B., Mille, A., and Chiron, B. (1995). Operator Decision aiding by Adaptation of Supervision Strategies. In Veloso, M. and Aamodt, A., editors, First International Conference on Case-Based Reasoning-ICCBR-95, pages 23–32, Sesimbra, Portugal. LNAI, vol. 1010, Springer, Berlin.

    Google Scholar 

  11. Goel, A. (1996). Meta cases: Explaining case-based reasoning. In Smith, I. and Faltings, B., editors, Third European Workshop on Case-Based Reasoning-EWCBR-96, pages 150–163, Lausanne, Suisse. LNAI, vol. 1168, Springer, Berlin.

    Chapter  Google Scholar 

  12. Jaczinski, M. and Trousse, B. (1998). An object-oriented framework for the design and implementation of case-based reasoners. In Proceedings of the 6th German Workshop on CBR (GWCBR-98).

    Google Scholar 

  13. Kamp, G., Lange, S., and Globig, C. (1998). Related areas, chapter 13, pages 327–351. In In [Lenz et al., 1998] LNAI 1400. Springer.

    Google Scholar 

  14. Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., and Wess, S. (1998). Case-Based Reasoning Technology, from foundations to applications. LNAI 1400. Springer, Berlin.

    Google Scholar 

  15. Mariño, O., Rechenmann, F., and Uvietta, P. (1990). Multiple perspectives and classification mechanism in object-oriented representation. In Proceedings of the 9th European Conference on Artificial Intelligence-ECAI’90, pages 425–430, Stockholm (SE). Pitman Publishing, London (GB).

    Google Scholar 

  16. Napoli, A., Lieber, J., and Curien, R. (1996). Classification-Based Problem-solving in Case-Based Reasoning. In Smith, I. and Faltings, B., editors, Third European Workshop on Case-Based Reasoning-EWCBR-96, pages 295–308, Lausanne, Suisse. LNAI, vol. 1168, Springer, Berlin.

    Chapter  Google Scholar 

  17. Newell, A. (1982). The Knowledge Level. Artificial Intelligence, 19(2):87–127.

    Article  Google Scholar 

  18. Plaza, E. and Arcos, J.-L. (1993). Noos: an integrated framework for problem solving and learning. Technical report, Institut d’investigació en Intelligència Artificial, Barcelona, Spain, Report IIIA-RR-97-02.

    Google Scholar 

  19. Slade, S. (1991). Case-Based Reasoning: A Research Paradigm. AI Magazine, 12(1):42–55.

    Google Scholar 

  20. Steels, L. (Summer 1990). Components of Expertise. AI Magazine, pages 28–49.

    Google Scholar 

  21. Wielinga, B.J., Schreiber, T., and Breuker, J. A. (1992). KADS: A Modelling Approach to Knowledge Engineering. Knowledge Acquisition, 4(1):136–145.

    Article  Google Scholar 

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Fuchs, B., Mille, A. (2000). Representing Knowledge for Case-Based Reasoning: The Rocade System. In: Blanzieri, E., Portinale, L. (eds) Advances in Case-Based Reasoning. EWCBR 2000. Lecture Notes in Computer Science, vol 1898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44527-7_9

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  • DOI: https://doi.org/10.1007/3-540-44527-7_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67933-2

  • Online ISBN: 978-3-540-44527-2

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