International Conference on Case-Based Reasoning

Case-Based Reasoning Research and Development pp 1-14 | Cite as

Case Base Maintenance in Preference-Based CBR

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9343)

Abstract

In preference-based CBR (Pref-CBR), problem solving experience is represented in the form of contextualized preferences, namely, preferences between candidate solutions in the context of a target problem to be solved. Since a potentially large number of such preferences can be collected in the course of each problem solving episode, case base maintenance clearly becomes an issue in Pref-CBR. In this paper, we therefore extend our Pref-CBR framework by another component, namely, a method for dynamic case base maintenance. The main goal of this method is to increase efficiency of case-based problem solving, by reducing the size of the case base, while maintaining performance. To illustrate the effectiveness of our approach, we present a case study in which Pref-CBR is used for the repetitive traveling salesman problem.

References

  1. 1.
    Abdel-Aziz, A., Cheng, W., Strickert, M., Hüllermeier, E.: Preference-based CBR: a search-based problem solving framework. In: Delany, S.J., Ontañón, S. (eds.) ICCBR 2013. LNCS, vol. 7969, pp. 1–14. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  2. 2.
    Abdel-Aziz, A., Strickert, M., Hüllermeier, E.: Learning solution similarity in preference-based CBR. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS, vol. 8765, pp. 17–31. Springer, Heidelberg (2014) Google Scholar
  3. 3.
    Bergmann, R., Wilke, W.: Towards a new formal model of transformational adaptation in case-based reasoning. In: Prade, H. (ed.) ECAI-98, 13th European Conference on Artificial Intelligence, pp. 53–57 (1998)Google Scholar
  4. 4.
    Craw, S., Massie, S., Wiratunga, N.: Informed case base maintenance: a complexity profiling approach. In: Proceedings AAAI-2007, Twenty-Second National Conference on Artificial Intelligence, 22–26 July 2007, Vancouver, British Columbia, Canada, pp. 1618–1621 (2007)Google Scholar
  5. 5.
    Cummins, L., Bridge, D.: On dataset complexity for case base maintenance. In: Ram, A., Wiratunga, N. (eds.) ICCBR 2011. LNCS, vol. 6880, pp. 47–61. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  6. 6.
    Cunningham, P., Smyth, B., Hurley, N.: On the use of CBR in optimisation problems such as the TSP. Technical report TCD-CS-95-19, Trinity College Dublin, Department of Computer Science (1995)Google Scholar
  7. 7.
    Erfani, H.: Integrating case-based reasoning, knowledge-based approach and TSP algorithm for minimum tour finding. J. Appl. Math. Islam. Azad Univ. Lahijan 3(9), 49–59 (2006)Google Scholar
  8. 8.
    Gates, G.W.: The reduced nearest neighbor rule. IEEE Trans. Inf. Theor. 18(3), 431–433 (1972)CrossRefGoogle Scholar
  9. 9.
    Hart, P.: The condensed nearest neighbor rule. IEEE Trans. Inf. Theor. 14(3), 515–516 (1968)CrossRefGoogle Scholar
  10. 10.
    Hüllermeier, E., Schlegel, P.: Preference-based CBR: first steps toward a methodological framework. In: Ram, A., Wiratunga, N. (eds.) ICCBR 2011. LNCS, vol. 6880, pp. 77–91. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  11. 11.
    Jalali, V., Leake, D.: Adaptation-guided case base maintenance. In: Proceedings AAAI, National Conference on Artificial Intelligence (2014)Google Scholar
  12. 12.
    Kraay, D.R., Harker, P.T.: Case-based reasoning for repetitive combinatorial optimization problems, part I: framework. J. Heuristics 2, 55–85 (1996)CrossRefGoogle Scholar
  13. 13.
    Lawanna, A., Daengdej, J.: Hybrid technique and competence-preserving case deletion methods for case maintenance in case-based reasoning. Int. J. Eng. Sci. Technol. 2(4), 492–497 (2010)Google Scholar
  14. 14.
    Lupiani, E., Juarez, J.M., Palma, J.: Evaluating case-base maintenance algorithms. Knowl. Based Syst. 67, 180–194 (2014)CrossRefGoogle Scholar
  15. 15.
    Ontañón, S., Plaza, E.: Justification-based selection of training examples for case base reduction. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 310–321. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  16. 16.
    Salamó, M., Golobardes, E.: Rough sets reduction techniques for case-based reasoning. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 467–482. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  17. 17.
    Salamo, M., Golobardes, E.: Hybrid deletion policies for case base maintenance. In: Proceedings of FLAIRS-2003, pp. 150–154 (2003). Enginyeria Arquitectura, and La SalleGoogle Scholar
  18. 18.
    Smiti, A., Elouedi, Z.: Overview of maintenance for case based reasoning systems. Int. J. Comput. Appl. 32(2), 49–56 (2011)Google Scholar
  19. 19.
    Smyth, B., Keane, T.: Remembering to forget. In: Mellish, C.S. (ed.) Proceedings International Joint Conference on Artificial Intelligence, pp. 377–382, Morgan Kaufmann (1995)Google Scholar
  20. 20.
    Smyth, B.: Case-base maintenance. In: del Pobil, A.P., Mira, J., Ali, M. (eds.) Tasks and Methods in Applied Artificial Intelligence. LNCS, vol. 1416, pp. 507–516. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  21. 21.
    Zhu, J., Yang, Q.: Remembering to add: competence-preserving case-addition policies for case-base maintenance. In: Proceedings IJCAI-99, 16th International Joint Conference on Artificial Intelligence, pp. 234–239. Morgan Kaufmann Publishers Inc, San Francisco, CA, USA (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of MarburgMarburgGermany
  2. 2.Department of Computer ScienceUniversity of PaderbornPaderbornGermany

Personalised recommendations