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Applying Belief Revision to Case-Based Reasoning

  • Julien CojanEmail author
  • Jean Lieber
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 548)

Abstract

Adaptation is a task of case-based reasoning (CBR) that aims at modifying a case to solve a new problem. Now, belief revision deals also about modifications. This chapter studies how some results about revision can be applied to formalize adaptation and, more widely, CBR. Revision operators based on distances are defined in formalisms frequently used in CBR and applied to define an adaptation operator that takes into account the domain knowledge and the adaptation knowledge. This approach to adaptation is shown to generalize some other approaches to adaptation, such as rule-based adaptation.

Keywords

Domain Knowledge Propositional Logic Belief Revision Belief Base Target Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Riesbeck, C.K., Schank, R.C.: Inside Case-Based Reasoning. Lawrence Erlbaum Associates Inc., Hillsdale (1989)Google Scholar
  2. 2.
    Alchourrón, C.E., Gärdenfors, P., Makinson, D.: On the logic of theory change: partial meet functions for contraction and revision. J. Symbolic Logic 50, 510–530 (1985)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Maximini, K., Maximini, R., Bergmann, R.: An investigation of generalized cases. In: Ashley, K.D., Bridge, D. (eds.) Proceedings of the 5th International Conference on Case Base Reasoning (ICCBR’03), vol. 2689 of LNAI., pp. 261–275. Springer, Trondheim (June 2003)Google Scholar
  4. 4.
    Carbonell, J.G.: Learning by analogy: formulating and generalizing plans from past experience. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning, An Artificial Intelligence Approach, pp. 137–161. Morgan Kaufmann, Inc. (1983)Google Scholar
  5. 5.
    Carbonell, J.G.: Derivational analogy: a theory of reconstructive problem solving and expertise acquisition. In: Machine Learning, vol. 2, pp. 371–392. Morgan Kaufmann Inc. (1986)Google Scholar
  6. 6.
    Katsuno, H., Mendelzon, A.: Propositional knowledge base revision and minimal change. Artif Intell 52(3), 263–294 (1991)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Dalal, M.: Investigations into a theory of knowledge base revision: preliminary report. In: AAAI, pp. 475–479 (1988)Google Scholar
  8. 8.
    Konieczny, S., Lang, J., Marquis, P.: DA\(^2\) merging operators. Artifi. Intell. 157(1–2), 49–79 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Lieber, J.: Application of the revision theory to adaptation in case-based reasoning: the conservative adaptation. In: Proceedings of the 7th International Conference on Case-Based Reasoning (ICCBR-07). Lecture Notes in Artificial Intelligence 4626, pp. 239–253. Springer, Belfast (2007)Google Scholar
  10. 10.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, Inc., San Mateo (1993)Google Scholar
  11. 11.
    Karmarkar, N.: A new polynomial-time algorithm for linear programming. Combinatorica 4(4), 373–396 (1984)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Cojan, J., Lieber, J.: Belief merging-based case combination. In: Case-Based Reasoning Research and Development (ICCBR 2009), pp. 105–119 (2009)Google Scholar
  13. 13.
    Blansché, A., Cojan, J., Dufour Lussier, V., Lieber, J., Molli, P., Nauer, E., Skaf Molli, H., Toussaint, Y.: TAAABLE 3: Adaptation of ingredient quantities and of textual preparations. In: 18h International Conference on Case-Based Reasoning—ICCBR 2010, “Computer Cooking Contest” Workshop Proceedings (2010)Google Scholar
  14. 14.
    Lieber, J., Napoli, A.: Correct and complete retrieval for case-based problem-solving. In: Prade, H., (ed.) Proceedings of the 13th European Conference on Artificial Intelligence (ECAI-98), Brighton, United Kingdom, pp. 68–72 (1998)Google Scholar
  15. 15.
    Craw, S., Wiratunga, N., Rowe, R.C.: Learning adaptation knowledge to improve case-based reasoning. Artifi. Intell. 170(16–17), 1175–1192 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    d’Aquin, M., Badra, F., Lafrogne, S., Lieber, J., Napoli, A., Szathmary, L.: Case base mining for adaptation knowledge acquisition. In: Veloso, M.M., (ed.) Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI’07), pp. 750–755. Morgan Kaufmann, Inc. (2007)Google Scholar
  17. 17.
    Jarmulak, J., Craw, S., Rowe, R.: Using case-base data to learn adaptation knowledge for design. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI’01), pp. 1011–1016. Morgan Kaufmann, Inc. (2001)Google Scholar
  18. 18.
    Leake, D.B., Kinley, A., Wilson, D.C.: Acquiring case adaptation knowledge: a hybrid approach. AAAI/IAAI 1, 684–689 (1996)Google Scholar
  19. 19.
    Stahl, A., Bergmann, R.: Applying recursive CBR for the customization of structure products in an electronic shop. In: Blanzieri, E., Portinale, L., (eds.) Advances in Case-Based Reasoning—Proceedings of the fifth European Workshop on Case-Based Reasoning (EWCBR-2k). Lecture Notes in Artificial Intelligence 1898, pp. 297–308. Springer (2000)Google Scholar
  20. 20.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P. (eds.): The Description Logic Handbook. Cambridge University Press, Cambridge (2003)Google Scholar
  21. 21.
    Flouris, G., Plexousakis, D., Antoniou, G.: On applying the AGM theory to DLs and OWL. In: Gil, Y., Motta, E., (eds.) Proceedings of the 4th International Semantic Web Conference (ISWC 2005). LNCS 3729, pp. 216–231. Springer (November 2005)Google Scholar
  22. 22.
    Kalyanpur, A., Parsia, B., Sirin, E., Hendler, J.: Debugging unsatisfiable classes in OWL ontologies. Web Semant.: Sci. Serv. Agents World Wide Web 3(4), 268–293 (2005)CrossRefGoogle Scholar
  23. 23.
    Cojan, J., Lieber, J.: An algorithm for adapting cases represented in \({\cal {A}}{\cal {L}}{\cal {C}}\). In: 22th Internationational Joint Conference on Artificial Intelligence, Barcelone Espagne (07 2011)Google Scholar
  24. 24.
    Allen, J.F.: An interval-based representation of temporal knowledge. In: Proceedings 7th International Joint Conference on Artificial Intelligence (IJCAI 1981), pp. 221–226 (1981)Google Scholar
  25. 25.
    Randell, D.A., Cui, Z., Cohn, A.G.: A spatial logic based on regions and connection. In: Knowledge Representation, pp. 165–176 (1992)Google Scholar
  26. 26.
    Condotta, J.F., Kaci, S., Marquis, P., Schwind, N.: A syntactical approach to qualitative constraint networks merging. In: Proceedings of the 17th LPAR (Logic for Programming, Artificial Intelligence and Reasoning), pp. 233–247 (2010)Google Scholar
  27. 27.
    Dufour-Lussier, V., Le Ber, F., Lieber, J., Martin, L.: Adapting spatial and temporal cases. In: Ian Watson, B.D.A., (ed.) International Conference for Case-Based Reasoning. Volume 7466 of Lecture Notes in Artificial Intelligence., Lyon, France, Amélie Cordier, Marie Lefevre, pp. 77–91. Springer (September 2012)Google Scholar
  28. 28.
    Pujari, A.K., Kumari, G.V., Sattar, A.: INDU: An interval and duration network. Advanced Topics in Artificial Intelligence, pp. 291–303 (1999)Google Scholar
  29. 29.
    Ligozat, G.: On generalized interval calculi. In: Proceedings of the 9th National Conference of the American Association for Artificial Intelligence (AAAI), pp. 234–240. AAAI Press/MIT Press, Anaheim (1991)Google Scholar
  30. 30.
    Smyth, B., Keane, M.T.: Using adaptation knowledge to retrieve and adapt design cases. Knowl.-Based Syst. 9(2), 127–135 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.LORIA, UMR 7503Université de LorraineVandœuvre-lés-NancyFrance
  2. 2.CNRSVandœuvre-lés-NancyFrance
  3. 3.INRIAVillers-lés-NancyFrance

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