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Explanation-based similarity: A unifying approach for integrating domain knowledge into case-based reasoning for diagnosis and planning tasks

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Book cover Topics in Case-Based Reasoning (EWCBR 1993)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 837))

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Abstract

Case-based problem solving can be significantly improved by applying domain knowledge (in opposition to problem solving knowledge), which can be acquired with reasonable effort, to derive explanations of the correctness of a case. Such explanations, constructed on several levels of abstraction, can be employed as the basis for similarity assessment as well as for adaptation by solution refinement. The general approach for explanation-based similarity can be applied to different real world problem solving tasks such as diagnosis and planning in technical areas. This paper presents the general idea as well as the two specific, completely implemented realizations for a diagnosis and a planning task.

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References

  1. Agnar Aamodt. A Knowledge-Intensive, Integrated Approach to Problem Solving and Sustained Learning. PhD thesis, University of Trondheim, 1991.

    Google Scholar 

  2. A. Aamodt. Explanation-driven retrieval, reuse and learning from cases. In M. M. Richter, S. Wess, K.D. Althoff, and F. Maurer, editors, Preprints of the First European Workshop on Case-Based Reasoning (EWCBR-93), volume II, pages 279–284. University of Kaiserslautern (Germany), 1993.

    Google Scholar 

  3. K.-D. Althoff, Stefan Wess, B. Bartsch-Spörl, D. Janetzko, F. Maurer, and A. Voss. Fallbasiertes Schliessen in Expertensystemen: Welche Rolle spielen Fälle für wissensbasierte Systeme? KI — Künstliche Intelligenz, 92(4), December 1992.

    Google Scholar 

  4. Ray Bareiss. Exemplar-Based Knowledge Acquisition: A unified Approach to Concept Representation, Classification and Learning. Academic Press, 1989.

    Google Scholar 

  5. R. Barletta and W. Mark. Explanation-based indexing of cases. In J. Kolodner, editor, Proceedings of the DARPA Workshop on Case-Based Reasoning, pages 50–60, San Mateo, California, 1988. Morgan Kaufmann Publishers, Inc.

    Google Scholar 

  6. R. Bergmann. Knowledge acquisition by generating skeletal plans. In F. Schmalhofer, G. Strube, and Th. Wetter, editors, Contemporary Knowledge Engineering and Cognition, pages 125–133, Heidelberg, 1992. Springer.

    Google Scholar 

  7. R. Bergmann. Integrating abstraction, explanation-based learning from multiple examples and hierarchical clustering with a performance component for planning. In Enric Plaza, editor, Proceedings of the ECML-93 Workshop on Integrated Learning Architectures (ILA-93), Vienna, Austria, 1993.

    Google Scholar 

  8. S. Boblin and R. L. Kashyap. Generating fault hypotheses with a functional model in machine-fault diagnosis. Applied Artificial Intelligence, 6:353–382, 1992.

    Google Scholar 

  9. J. G. Carbonell. Derivational analogy: A theory of reconstructive problem solving and expertise aquisition. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine learning: An artificial intelligence approach, volume 2, chapter 14, pages 371–392. Morgan Kaufmann, Los Altos, CA, 1986.

    Google Scholar 

  10. E. Feigenbaum and P. McCorduck. The fifth generation. Addison Wesley, Reading MA, 1983.

    Google Scholar 

  11. R. E. Fikes and N. J. Nilsson. Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2:189–208, 1971.

    Google Scholar 

  12. R.C. Holte. Commentary to: Protos an exemplar-based learning apprentice. In Y. Kodratoff and R.S. Michalski, editors, Machine Learning: An Artificial Intelligence Approach, volume 3, chapter 4, pages 128–139. Morgan Kaufmann Publishers, 1990.

    Google Scholar 

  13. D. Janetzko, S. Wess, and E. Melis. Goal-driven similarity assessment. In H.J. Ohlbach, editor, GWAI-92 16th German Workshop on Artificial Intelligence, volume 671 of Springer Lecture Notes on AI, 1992.

    Google Scholar 

  14. Alex M. Kass and David B. Leake. Case-Based Reasoning Applied to Constructing Explanations. In Janet L. Kolodner, editor, Proceedings Case-Based Reasoning Workshop, pages 190–208, San Mateo, California, 1988. Morgan Kaufmann Publishers.

    Google Scholar 

  15. C. A. Knoblock. Learning abstraction hierarchies for problem solving. In MIT Press, editor, Proceedings Eighth National Conference on Artificial Intelligence, volume 2, pages 923–928, London, 1990. MIT Press.

    Google Scholar 

  16. Janet L. Kolodner. Case-based reasoning. Morgan Kaufmann, 1993.

    Google Scholar 

  17. P. Koton. Reasoning about evidence in causal explanations. In J. Kolodner, editor, Proceedings of the DARPA Workshop on Case-Based Reasoning, pages 260–270, San Mateo, California, 1988. Morgan Kaufmann Publishers, Inc.

    Google Scholar 

  18. S. Minton. Quantitativ results concerning the utility of explanation-based learning. Artifical Intelligence, 42:363–391, 1990.

    Google Scholar 

  19. T. M. Mitchell, R. M. Keller, and S. T. Kedar-Cabelli. Explanation-based generalization: A unifying view. Machine Learning, 1(1):47–80, 1986.

    Google Scholar 

  20. Allen Newell. The knowledge level. Artificial Intelligence, 18:87–127, 1982.

    Google Scholar 

  21. Jürgen Paulokat and Stefan Wess. Fallauswahl und fallbasierte Steuerung bei der nichtlinearen hierarchischen Planung. In A. Horz, editor, Beitr” age zum 7. Workshop Planen und Konfigurieren, number 723 in Arbeitspapiere der GMD, pages 109–120, 1993.

    Google Scholar 

  22. G. Pews and S. Wess. Combining model-based approaches and case-based reasoning for similarity assessment and case adaptation in diagnositc applications. In M. M. Richter, S. Wess, K.D. Althoff, and F. Maurer, editors, Preprints of the First European Workshop on Case-Based Reasoning (EWCBR-93), volume II, pages 325–328. University of Kaiserslautern, 1993.

    Google Scholar 

  23. E.D. Sacerdoti. Planning in a hierarchy of abstraction spaces. Artificial Intelligence, 5:115–135, 1974.

    Google Scholar 

  24. M. M. Veloso and J. G. Carbonell. Towards scaling up machine learning: A case study with derivational analogy in PRODIGY. In Steven Minton, editor, Machine Learning Methods for Planning, chapter 8, pages 233–272. Morgan Kaufmann, 1993.

    Google Scholar 

  25. B. Wielinga, W. VandeVelde, G. Schreiber, and H. Akkermans. Towards a unification of knowledge modelling approaches. In Proceedings of the 7th Banff Knowledge Acquisition for Knowledge-based Systems Workshop, 1992.

    Google Scholar 

  26. W. Wilke. Entwurf und Implementierung eines Algorithmus zum wissensintensiven Lernen von Planabstraktionen nach der PABS-Methode. Projektarbeit, Universität Kaiserslautern, 1993.

    Google Scholar 

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Authors and Affiliations

  1. Dept. of Computer Science, University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Ralph Bergmann, Gerd Pews & Wolfgang Wilke

Authors
  1. Ralph Bergmann
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  2. Gerd Pews
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  3. Wolfgang Wilke
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Editor information

Stefan Wess Klaus-Dieter Althoff Michael M. Richter

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© 1994 Springer-Verlag Berlin Heidelberg

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Bergmann, R., Pews, G., Wilke, W. (1994). Explanation-based similarity: A unifying approach for integrating domain knowledge into case-based reasoning for diagnosis and planning tasks. In: Wess, S., Althoff, KD., Richter, M.M. (eds) Topics in Case-Based Reasoning. EWCBR 1993. Lecture Notes in Computer Science, vol 837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58330-0_86

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  • DOI: https://doi.org/10.1007/3-540-58330-0_86

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58330-1

  • Online ISBN: 978-3-540-48655-8

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