Collaborative Case Retention Strategies for CBR Agents

  • Santiago Ontañón
  • Enric Plaza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2689)


Empirical experiments have shown that storing every case does not automatically improve the accuracy of a CBR system. Therefore, several retain policies have been proposed in order to select which cases to retain. However, all the research done in case retention strategies is done in centralized CBR systems. We focus on multiagent CBR systems, where each agent has a local case base, and where each agent can interact with other agents in the system to solve problems in a collaborative way. We propose several case retention strategies that directly deal with the issue of being in a multiagent CBR system. Those case retention strategies combine ideas from the CBR case retain strategies and from the active learning techniques. Empirical results show that strategies that use collaboration with other agents outperform those strategies where the agents work in isolation. We present experiments in two di.erent scenarios, the first one allowing multiple copies of one case and the second one only allowing one copy of each case. Although it may seem counterintuitive, we show and explain why not allowing multiple copies of each case achieves better results.


Case Base Active Learning Multiagent System Vote Scheme Approval Vote 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Agnar Aamodt and Enric Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications, 7(1):39–59, 1994.Google Scholar
  2. [2]
    David W. Aha, Dennis Kibler, and Marc K. Albert. Instance-based learning algorithms. Machine Learning, 6(1):37–66, 1991.Google Scholar
  3. [3]
    Steven J. Brams and Peter C. Fishburn. Approval Voting. Birkhauser, 1983.Google Scholar
  4. [4]
    David A. Cohn, Les Atlas, and Richard E. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201–221, 1994.Google Scholar
  5. [5]
    L. K. Hansen and P. Salamon. Neural networks ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:993–1001, 1990.CrossRefGoogle Scholar
  6. [6]
    Anders Krogh and Jesper Vedelsby. Neural network ensembles, cross validation, and active learning. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances in Neural Information Processing Systems, volume 7, pages 231–238. The MIT Press, 1995.Google Scholar
  7. [7]
    David B. Leake and David C. Wilson. Remembering why to remember: Performance-guided case-base maintenance. In EWCBR-2000, LNAI, pages 161–172. Springer Verlag, 2000.Google Scholar
  8. [8]
    S. Markovich and P. Scott. The role of forgetting in learning. In ICML-88, pages 459–465. Morgan Kaufman, 1988.Google Scholar
  9. [9]
    S. Ontañón and E. Plaza. Learning when to collaborate among learning agents. In ECML-2001, LNAI, pages 394–405. Springer-Verlag, 2001.Google Scholar
  10. [10]
    Santiago Ontañón and Enric Plaza. Learning to form dynamic committees. In Int. Conf. Autonomous Agents and Multiagent Systems AAMAS’03, 2003.Google Scholar
  11. [11]
    Enric Plaza and Santiago Ontañón. Ensemble case-based reasoning: Collaboration policies for multiagent cooperative cbr. In I. Watson and Q. Yang, editors, In Case-Based Reasoning Research and Development: ICCBR-2001, number 2080 in LNAI, pages 437–451. Springer-Verlag, 2001.Google Scholar
  12. [12]
    H. S. Seung, Manfred Opper, and Haim Sompolinsky. Query by committee. In Computational Learing Theory, pages 287–294, 1992.Google Scholar
  13. [13]
    B. Smyth. The utility problem analysed: A case-based reasoning persepctive. In EWCBR-96, LNAI, pages 234–248. Springer Verlag, 1996.Google Scholar
  14. [14]
    Barry Smyth and Mark T. Keane. Remembering to forget: A competencepreserving case deletion policy for case-based reasoning systems. In IJCAI-95, pages 377–382, 1995.Google Scholar
  15. [15]
    Jun Zhu and Qiang Yang. Remembering to add: Competence-preserving caseaddition policies for case base maintenance. In IJCAI-99, pages 234–241, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Santiago Ontañón
    • 1
  • Enric Plaza
    • 1
  1. 1.CSIC, Spanish Council for Scientific ResearchIIIA, Artificial Intelligence Research InstituteBellaterra, CataloniaSpain

Personalised recommendations