Competence Guided Casebase Maintenance for Compositional Adaptation Applications

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9969)

Abstract

A competence guided casebase maintenance algorithm retains a case in the casebase if it is useful to solve many problems and ensures that the casebase is highly competent in the global sense. In this paper, we address the compositional adaptation process (of which single case adaptation is a special case) during casebase maintenance by proposing a case competence model for which we propose a measure called retention score to estimate the retention quality of a case. We also propose a revised algorithm based on the retention score to estimate the competent subset of the casebase. We used regression datasets to test the effectiveness of the competent subset obtained from the proposed model. We also applied this model in a tutoring application and analyzed the competent subset of concepts in tutoring resources. Empirical results show that the proposed model is effective and overcomes the limitation of footprint based competence model in compositional adaptation applications.

Keywords

Casebase maintenance Case competence Footprint based competence model Compositional adaptation 

References

  1. 1.
    Arshadi, N., Badie, K.: A compositional approach to solution adaptation in case-based reasoning and its application to tutoring library. In: Proceedings of 8th German Workshop on Case-Based Reasoning (2000)Google Scholar
  2. 2.
    Atzmueller, M., Baumeister, J., Puppe, F., Shi, W., Barnden, J.A.: Case-based approaches for diagnosing multiple disorders. In: FLAIRS, pp. 154–159 (2004)Google Scholar
  3. 3.
    Lekkas, G.P., Avouris, N.M., Viras, L.G.: Case-based reasoning in environmental monitoring applications. Appl. Artif. Intell. Int. J. 8(3), 359–376 (1994)CrossRefGoogle Scholar
  4. 4.
    Lieber, J.: A criterion of comparison between two case bases. In: Haton, J.-P., Keane, M., Manago, M. (eds.) EWCBR 1994. LNCS, vol. 984, pp. 87–100. Springer, Heidelberg (1995). doi:10.1007/3-540-60364-6_29 CrossRefGoogle Scholar
  5. 5.
    Markovitch, S., Scott, P.D.: Information filtering: selection mechanisms in learning systems. Mach. Learn. 10(2), 113–151 (1993)Google Scholar
  6. 6.
    Massé, A.B., Chicoisne, G., Gargouri, Y., Harnad, S., Picard, O., Marcotte, O.: How is meaning grounded in dictionary definitions? In: Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing, pp. 17–24 (2008)Google Scholar
  7. 7.
    Mathew, D., Eswaran, D., Chakraborti, S.: Towards creating pedagogic views from encyclopedic resources. In: Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 190–195 (2015)Google Scholar
  8. 8.
    Müller, G., Bergmann, R.: Compositional adaptation of cooking recipes using workflow streams. In: Computer Cooking Contest, Workshop Proceedings ICCBR (2014)Google Scholar
  9. 9.
    Müller, G., Bergmann, R.: Workflow streams: a means for compositional adaptation in process-oriented CBR. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS (LNAI), vol. 8765, pp. 315–329. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11209-1_23 Google Scholar
  10. 10.
    Patterson, D., Rooney, N., Galushka, M.: A regression based adaptation strategy for case-based reasoning. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, pp. 87–92 (2012)Google Scholar
  11. 11.
    Agrawal, R., Chakraborty, S., Gollapudi, S., Kannan, A., Kenthapadi, K.: Quality of textbooks: an empirical study. In: ACM Symposium on Computing for Development (2012)Google Scholar
  12. 12.
    Reinartz, T., Ioannis, I., Thomas, R.: Review and restore for case base maintenance. Comput. Intell. 17(2), 214–234 (2001)CrossRefGoogle Scholar
  13. 13.
    Smyth, B., Keane, M.T.: Remembering to forget. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), pp. 377–382 (1995)Google Scholar
  14. 14.
    Smyth, B., McKenna, E.: Modelling the competence of case-bases. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS, vol. 1488, pp. 208–220. Springer, Heidelberg (1998). doi:10.1007/BFb0056334 CrossRefGoogle Scholar
  15. 15.
    Smyt, B., McKenna, E.: Footprint-based retrieval. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS, vol. 1650, pp. 343–357. Springer, Heidelberg (1999). doi:10.1007/3-540-48508-2_25 CrossRefGoogle Scholar
  16. 16.
    Wilke, W., Bergmann, R.: Techniques and knowledge used for adaptation during case-based problem solving. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds.) IEA/AIE 1998. LNCS, vol. 1416, pp. 497–506. Springer, Heidelberg (1998). doi:10.1007/3-540-64574-8_435 CrossRefGoogle Scholar
  17. 17.
    Ye, S., Chua, T., Lu, J.: Summarizing definition from Wikipedia. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on NLP of the AFNLP, pp. 199–207 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia

Personalised recommendations