Self-organising Hierarchical Retrieval in a Case-Agent System

  • Ian Watson
  • Jens Trotzky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)


This paper describes the implementation of a distributed case-agent system where a case-base is comprised of a set of agents, where each computational agent is a case, rather than the standard case-base reasoning model where a single computational agent accesses a single case-base. This paper demonstrates a set of features that can be modelled in a case-agent system focusing on distributed self-organising hierarchical retrieval. The performance of the system is evaluated and compared to that of a well recognised hierarchical retrieval method (i.e., footprint-based retrieval). The emergent properties of the case-agent architecture are discussed.


Multiagent System Similarity Threshold Retrieval Algorithm Similarity Metrics 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.


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  1. Aamont, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AICOM 7(1), 39–58 (1994)Google Scholar
  2. Aha, D.W., Kibler, D., Albert, M.K.: Instance-Based Learning Algorithms. Machine Learning 6, 37–66 (1991)Google Scholar
  3. Amato, G.F., Rabitti, F., Savino, P., Zezula, P.: Region proximity in metric spaces and its use for approximate similarity search. ACM Trans. Inf. Syst. 21(2), 192–227 (2003)CrossRefGoogle Scholar
  4. Branting, K., Aha, D.: Stratified Case-Based Reasoning: Reusing Hierarchical Problem Solving Episodes. In: Proc. of the 14th Int. Joint Conf. on Artificial Intelligence, Montreal, Canada, pp. 20–25 (1995)Google Scholar
  5. Brown, M.G.: An Underlying Memory Model to Support Case Retrieval. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 132–143. Springer, Heidelberg (1994)Google Scholar
  6. Brown, M.G., Watson, I., Filer, N.: Separating the Cases from the Data; Towards More Flexible Case-Based Reasoning. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS (LNAI), vol. 1010. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  7. Burke, R., Hammond, K., Kulyukin, V., Lytinen, S., Tomuro, N., Schoenberg, S.: Question answering from frequently-asked question files: Experiences with the FAQ Finder system. AI Magazine 18(2), 57–66 (1997)Google Scholar
  8. Ciaccia, P., Patella, M.: Searching in metric spaces with user-defined and approximate distances. ACM Trans. Database Syst. 27(4), 398–437 (2002)CrossRefGoogle Scholar
  9. Dasarathy, B.V.: Nearest Neighbor Norms: NN Pattern Classification Techniques. IEEE Press, Los Alamos (1991)Google Scholar
  10. Gennaro, C., Savino, P., Zezula, P.: Similarity search in metric databases through hashing. In: Proc. of the 2001 ACM workshops on Multimedia: multimedia information retrieval, Ottawa, Canada, pp. 1–5. ACM Press, New York (2001)CrossRefGoogle Scholar
  11. Hammond, K., Burke, R., Young, B.: The FindMe approach to assisted browsing. IEEE Expert 12(4), 32–40 (1997)CrossRefGoogle Scholar
  12. Hart, P.E.: The Condensed Nearest Neighbor Rule. IEEE Trans. on Info. Theory 14, 515–516 (1967)CrossRefGoogle Scholar
  13. Hayes, C., Cunningham, P., Doyle, M.: Distributed CBR using XML. In: Proc. of the UKCBR Workshop: Intelligent Systems & Electronic Commerce (1998)Google Scholar
  14. Jaczynski, M., Trousse, B.: An object-oriented framework for the design and the implementation of case-based reasoners. In: Gierl, L., Lenz, M. (eds.) Proc. of the 6th German Workshop on Case-Based Reasoning, Berlin, Germany (1998)Google Scholar
  15. Leake, D.: Case-Based Reasoning: Experiences, Lessons and Future Dirctions. AAAI Press/MIT Press (1996)Google Scholar
  16. Leake, D., Sooriamurthi, R.: When two case bases are better than one: Exploiting multiple case bases. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 321–335. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  17. McGinty, L., Smyth, B.: Collaborative case-based reasoning: Applications in personalized route planning. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 362–376. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  18. Ontanon, S., Plaza, E.: A bartering aproach to improve multiagent learning. In: Proc. of the 1st Int. Joint Conf. on Autonomous Agents & Multiagent Systems, pp. 386–393 (2002)Google Scholar
  19. Ontanon, S., Plaza, E.: Learning to form dynamic committees. In: Proc. of the 2nd Int. Joint Conf. on Autonomous Agents and Multiagent Systems, pp. 504–511 (2003)Google Scholar
  20. Plaza, E., McGinty, L.: Distributed Case-Based Reasoning. The Knowledge Engineering Review (to appear, 2006)Google Scholar
  21. Plaza, E., Ontanon, S.: Ensemble case-based reasoning: Collaboration policies for multiagent cooperative CBR. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 437–451. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  22. Prasad, M.V.N., Lesser, V.R., Lander, S.E.: Retrieval and reasoning in distributed case bases. Journal of Visual Communication & Image Representation, Special Issue on Digital Libraries 7(1), 74–87 (1996)Google Scholar
  23. Redmond, M.: Distributed cases for case-based reasoning: Facilitating use of multiple cases. In: Dietterich, T., Swartout, W. (eds.) Proc. of the 18th National Conf. on Artificial Intelligence, pp. 304–309. AAAI Press/The MIT Press (1990)Google Scholar
  24. Smyth, B., Cunningham, P.: The utility problem analysed: A case-based reasoning perspective. In: Smith, I., Faltings, B.V. (eds.) EWCBR 1996. LNCS (LNAI), vol. 1168, pp. 392–399. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  25. Smyth, B., McKenna, E.: Footprint Based Retrieval. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS (LNAI), vol. 1650, pp. 343–357. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  26. Watson, I.: Case-Agents: A Novel Architecture for Case-Based Agents. In: Proc. of the 17th Int. Florida Artificial Intelligence Research Society Conf., pp. 202–206. AAAI Press, Menlo Park (2004)Google Scholar
  27. Watson, I., Gardingen, D.: A distributed case-based reasoning application for engineering sales support. In: Dean, T. (ed.) Proc. of the 16th Int. Joint Conf. on Artificial Intelligence, pp. 600–605. Morgan Kaufmann, San Francisco (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ian Watson
    • 1
  • Jens Trotzky
    • 1
  1. 1.Dept. of Computer ScienceUniversity of AucklandNew Zealand

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