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Towards a Unified Theory of Adaptation in Case-Based Reasoning

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Case-Based Reasoning Research and Development (ICCBR 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1650))

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Abstract

Case-based reasoning exploits memorized problem solving episodes, called cases, in order to solve a new problem. Adaptation is a complex and crucial step of case-based reasoning which is generally studied in the restricted framework of an application domain. This article proposes a first analysis of case adaptation independently from a specific application domain. It proposes to combine the retrieval and adaptation steps in a unique planning process that builds an ordered sequence of operations starting from an initial state (the stated problem) and leading to a final state (the problem once solved). Thus, the issue of case adaptation can be addressed by studying the issue of plan adaptation. Finally, it is shown how case retrieval and case adaptation can be related thanks to reformulations and similarity paths.

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References

  1. R. Bergmann and W. Wilke. Building and Refining Abstract Planning Cases by Change of Representation Language. Journal of Artificial Intelligence Research, 3:53–118,1995.

    Google Scholar 

  2. R. Bergmann and W. Wilke. PARIS: Flexible Plan Adaptation by Abstraction and Refinement. In A. Voß, R. Bergmann, and B. Bartsch-Spörl, editors, Workshop on Adaptation in Case-Based Reasoning, ECAI-96, Budapest, Hungary, August 1996.

    Google Scholar 

  3. L.K. Branting and D.W. Aha. Stratified Case-Based Reasoning: Reusing Hierarchical Problem Solving Episodes. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI’95), Montréal, volume 1, pages 384–390, August 1995.

    Google Scholar 

  4. J.G. Carbonell. Derivational analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition. In Machine Learning, volume 2, chapter 14, pages 371–392._Springer-Verlag, 1986.

    Google Scholar 

  5. E. Charniak and D.V. McDermott. Introduction to Artificial Intelligence. Addison Wesley, Reading, Massachusetts, 1985.

    MATH  Google Scholar 

  6. B. Chiron and A. Mille. FrAide à la conception d’environnements de supervision par réutilisation de l’expérience. In JICAA’97, ROSCOFF, 20-22 Mai 1997, pages 181–187, 1997.

    Google Scholar 

  7. B. Fuchs, A. Mille, and B. Chiron. Operator decision aiding by adaptation of supervision strategies. In Lecture Notes in Artificial Intelligence vol 1010, First International Conference on Case-Based Reasoning, ICCBR’95, pages 23–32, Sesimbra, Portugal, 1995. Springer-Verlag, Berlin, Germany.

    Google Scholar 

  8. Kristian Hammond. Case-based planning: viewing planning as a memory task. Academic Press, San Diego, 1989.

    Google Scholar 

  9. S. Hanks and D.S. Weld. A Domain-Independent Algorithm for Plan Adaptation. Journal of Artificial Intelligence Research, 2:3191–360, 1995.

    Google Scholar 

  10. O. Herbeaux and A. Mille. ACCELERE: un système d’aide à la conception de caoutchouc cellulaire exploitant la rèutilisation de l’expérience. Journal Européen des Systèmes Automatisés, 1998. Soumis au Journal Européen des Systèmes Automatis ás, disponible comme rapport de recherche.

    Google Scholar 

  11. K. Hua, I. Smith, and B. Faltings. Integrated Case-Based Building Design. In S. Wess, K.-D. Althoff, and M.M. Richter, editors, Topics in Case-Based Reasoning-First European Workshop (EWCBR’93), Kaiserslautern, Lecture Notes in Artificial Intelligence 837, pages 458–469. Springer Verlag, Berlin, 1994.

    Google Scholar 

  12. K. Hua, B. Faltings, and I. Smith. CADRE: case-based geometric design. Artificial Intelligence in Engineering, 10:171–183, 1996.

    Article  Google Scholar 

  13. L.H. Ihrig and S. Kambhampati. Storing and Indexing Plan Derivation through Explanation-based Analysis of Retrieval Failures. Journal of Artificial Intelligence Research, 7:161–198, 1997.

    Google Scholar 

  14. J. Koehler. Planning from Second Principles. Artificial Intelligence, 87:145–186, 1996.

    Article  MathSciNet  Google Scholar 

  15. B. Kumar and B. Raphael. Cadrem: A case based system for conceptual structural design. Engineering with Computers, 13(3):153–164, 1997.

    Article  Google Scholar 

  16. J.E. Laird, A. Newell, and P.S. Rosenbloom. SOAR: An Architecture for General Intelligence. AI Magazine, 33(1):1–64, 1987.

    Google Scholar 

  17. D.B. Leake, A. Kinley, and D. Wilson. Learning to Improve Case Adaptation by Introspective Reasoning and CBR. In M. Veloso and A. Aamodt, editors, Case-Based Reasoning Research an Development. Proceedings of the First International Conference on Case-Based Reasoning-ICCBR-95, pages 229–240, Sesimbra, Portugal, 23–26 octobre 1995. Lecture Notes in Artificial Intelligence, volume 1010, Springer Verlag, Berlin.

    Google Scholar 

  18. D.B. Leake, A. Kinley, and D. Wilson. Acquiring case adaptation knowledge: A hybrid approach. In Proceedings of the 14th National Conference on Artificial Intelligence, Menlo Park, CA, pages 684–689.AAAI Press, Menlo Park, CA, 1996.

    Google Scholar 

  19. D.B. Leake, A. Kinley, and D. Wilson. A Case Study of Case-Based CBR. In D.B. Leake and E. Plaza, editors, Case-Based Reasoning Research and Development-Second International Conference, ICCBR’97, Providence, RI, USA, Lecture Notes in Artificial Intelligence 1266, pages 371–382. Springer Verlag, Berlin, 1997.

    Google Scholar 

  20. D.B. Leake, A. Kinley, and D. Wilson. Case-based similarity assessment: Estimating adaptability from experience. In Proceedings of the Fourteenth National Conference on Artificial Intelligence. AAAI Press, Menlo Park, CA, 1997.

    Google Scholar 

  21. D.B. Leake, A. Kinley, and D. Wilson. Learning to integrate multiple knowledge sources for case-based reasoning. In Proceedings of the 15th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1997.

    Google Scholar 

  22. D.B. Leake. Learning adaptation strategies by introspective reasoning about memory search. In AAAI93 Workshop on Case-Based Reasoning, pages 57–63, 1993.

    Google Scholar 

  23. D.B. Leake. Representing self-knowledge for introspection about memory search. In Proceedings of the AAAI Spring Symposium on Representing Mental States and Mechanisms, 1995.

    Google Scholar 

  24. J. Lieber and A. Napoli. Using Classification in Case-Based Planning. In W. Wahlster, editor, Proceedings of the 12th European Conference on Artificial Intelligence (ECAI’96),Budapest, Hungary, pages 132–136. John Wiley & Sons, Ltd., 1996.

    Google Scholar 

  25. J. Lieber and A. Napoli. Correct and Complete Retrieval for Case-Based Problem-Solving. In H. Prade, editor, Proceedings of the 13th European Conference on Artificial Intelligence (ECAI-98), Brighton, United Kingdom, pages 68–72, 1998.

    Google Scholar 

  26. E. Melis, J. Lieber, and A. Napoli. Reformulation in Case-Based Reasoning. In B. Smyth and P. Cunningham, editors, Fourth European Workshop on Case-Based Reasoning, EWCBR-98, Lecture Notes in Artificial Intelligence 1488, pages 172–183._Springer, 1998.

    Google Scholar 

  27. E. Melis. A model of analogy-driven proof-plan construction. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI’95), pages 182–189, Montréal, 1995.

    Google Scholar 

  28. A. Mille, J.-L. Di-Martino, and A. Michel. Adaptation:the key-point in Case Based Reasoning. A case study:Digester Programming Helping, 1995. presented at the Workshop on practical developments strategies for industrial strength Case Based Reasoning applications, 16th International Conference on Artificial Intelligence, IJCAI’95, Montreal, Canada.

    Google Scholar 

  29. A. Mille, B. Fuchs, and O. Herbeaux. A unifying framework for Adaptation in Case-Based Reasoning. In A. Voß, editor, Proceedings of the ECAI’96Workshop: Adaptation in Case-Based Reasoning, pages 22–28, 1996.

    Google Scholar 

  30. A. Mille, B. Fuchs, and B. Chiron. FrLe raisonnement fondé sur l’expérience: un nouveau paradigme en supervision industrielle ? á; parître dans la Revue d’Intelligence Artificielle, 1999.

    Google Scholar 

  31. A. Newell. Reasoning, Problem Solving, and Decision Processes: The Problem Space as a Fundamental Category. In R. Nickerson, editor, Attention and Performances VIII, pages 693–718. Lawrence Erlbaum Associates, Hillsdale, NJ, 1980.

    Google Scholar 

  32. G. Polya. How to Solve it. Doubleday Anchor Books, New York, NY, 1957.

    Google Scholar 

  33. L. Purvis and P. Pu. Adaptation Using Constraint Satisfaction Techniques. In M. Veloso and A. Aamodt, editors, Case-Based Reasoning Research And Development. Proceedings Of The First International Conference On Case-Based Reasoning-ICCBR-95, pages 289–300, Sesimbra, Portugal, 23–26 Octobre 1995. Lecture Notes In Artificial Intelligence, Volume 1010, Springer Verlag, Berlin.

    Chapter  Google Scholar 

  34. I. Smith, C. Lottaz, and B. Faltings. Spatial composition using case: Idiom. In Manuela Veloso and Agnar Aamodt, editors, Case-Based Reasoning Reasearch And Development, Iccbr’95, pages 88–97, Sesimbra (Portugal), Octobre 1995.

    Google Scholar 

  35. I. Smith, R. Stalker, and C. Lottaz. Interactive case-based spatial composition. 1996.

    Google Scholar 

  36. B. Smyth and M.T. Keane. Using adaptation knowledge to retrieve and adapt design cases. Knowledge-Based Systems, 9(2):127–135, 1996.

    Article  Google Scholar 

  37. B. Smyth. Case-Based Design. PhD thesis, Trinity College, University of Dublin, 1996.

    Google Scholar 

  38. M.M. Veloso. Planning and Learning by Analogical Reasoning. LNAI 886. Springer Verlag, Berlin, 1994.

    MATH  Google Scholar 

  39. A. Voß, editor. Proceedings of the ECAI’96 Workshop: Adaptation in Case-Based Reasoning, 1996.

    Google Scholar 

  40. A. Voß. Structural Adaptation with TOPO. In A. Voß, R. Bergmann, and B. Bartsch-Spörl, editors, Workshop on Adaptation in Case-Based Reasoning, ECAI-96, Budapest, Hungary, August 1996.

    Google Scholar 

  41. Angi Voß. How to solve complex problems with cases. Engineering applications of artificial intelligence, 9(4):377–384, 1996.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Université Lyon III, IAE, équipe Modeme, 15 Quai Claude Bernard, 69239 LYON cedex 02

    Béatrice Fuchs

  2. LORIA-UMR 7503, 239, 54506, Vandoeuvre-lés-Nancy Cedex

    Jean Lieber & Amedeo Napoli

  3. LISA-CPE, 43 Bd du 11 Novembre 1918, 69616 Villeurbanne Cedex

    Alain Mille

Authors
  1. Béatrice Fuchs
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  2. Jean Lieber
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  3. Alain Mille
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  4. Amedeo Napoli
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Editor information

Editors and Affiliations

  1. Fraunhofer Institute for Experimental Software Engineering (IESE), Sauerwiesen 6, D 67661, Kaiserslautern, Germany

    Klaus-Dieter Althoff

  2. Department of Computer Science, University of Kaiserslautern, P.O. Box 3049, D 67653, Kaiserslautern, Germany

    Ralph Bergmann

  3. Department of Computer Science, University of Wyoming, P.O. Box 3682, Laramie, WY, 82072, USA

    L.Karl Branting

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© 1999 Springer-Verlag Berlin Heidelberg

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Fuchs, B., Lieber, J., Mille, A., Napoli, A. (1999). Towards a Unified Theory of Adaptation in Case-Based Reasoning. In: Althoff, KD., Bergmann, R., Branting, L. (eds) Case-Based Reasoning Research and Development. ICCBR 1999. Lecture Notes in Computer Science, vol 1650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48508-2_8

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  • DOI: https://doi.org/10.1007/3-540-48508-2_8

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  • Print ISBN: 978-3-540-66237-2

  • Online ISBN: 978-3-540-48508-7

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