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Activating CBR Systems through Autonomous Information Gathering

  • Christina Carrick
  • Qiang Yang
  • Irene Abi-Zeid
  • Luc Lamontagne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1650)

Abstract

Most traditional CBR systems are passive in nature, adopting an advisor role in which a user manually consults the system. In this paper, we propose a system architecture and algorithm for transforming a passive interactive CBR system into an active, autonomous CBR system. Our approach is based on the idea that cases in a CBR system can be used to model hypotheses in a situation assessment application, where case attributes can be considered as questions or information tasks to be performed on multiple information sources. Under this model, we can use the CBR system to continually generate tasks that are planned for and executed based on information sources such as databases, the Internet or the user herself. The advantage of the system is that the majority of trivial or repeated questions to information sources can be done autonomously through information gathering techniques, and human users are only asked a small number of necessary questions by the system. We demonstrate the application of our approach to an equipment diagnosis domain. We show that the system integrates CBR retrieval with hierarchical query planning, optimization and execution.

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References

  1. 1.
    D. Aha and L. Breslow. Refining conversational case libraries. In Proceedings of the Second International Conference on Case-based Reasoning (ICCBR-97), Providence, RI, July 1997.Google Scholar
  2. 2.
    D.W. Aha, L.A. Breslow, and T. Maney. Supporting conversational case-based reasoning in an integrated reasoning framework. Technical Report AIC-98-006, Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence, Washington, DC, 1998.Google Scholar
  3. 3.
    D.W. Aha, T. Maney, and L.A. Breslow. Supporting dialogue inferencing in conversational case-based reasoning. Technical Report AIC-98-008, Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence, Washing-ton, DC, 1998.Google Scholar
  4. 4.
    K. Ashley. Modelling legal argument: Reasoning with cases and hypotheticals. MIT Press, Bradford Books, Cambridge, MA, 1990.Google Scholar
  5. 5.
    K. Ashley and E. Rissland. A case-based approach to modeling legal expertise. IEEE Expert, 3(3):70–77, 1988.CrossRefGoogle Scholar
  6. 6.
    O.M. Duschka and A.Y. Levy. Recursive plans for information gathering. In Proceedings of IJCAI-97, Nagoya, Japan, August 1997.Google Scholar
  7. 7.
    K. Erol, J. Hendler, and D.S. Nau. Htn planning: Complexity and expressivity. In Proceedings of the 12th National Conference on Artificial Intelligence (AAAI-94), pages 1123–1128, Seattle, WA, 1994. AAAI Press/The MIT Press.Google Scholar
  8. 8.
    M.R. Genesereth, A.M. Keller, and O.M. Duschka. Infomaster: An information integration system. In Proceedings of the ACM SIGMOD Conference, May 1997.Google Scholar
  9. 9.
    R.J.B. Jr., W. Bohrer, R. Brice, A. Cichocki, J. Fowler, A. Helal, V. Kashyap, T. Ksiezyk, G. Martin, M. Nodine, M. Rashid, M. Rusinkiewicz, R. Shea, C. Unnikrishnan, A. Unruh, and D. Woelk. InfoSleuth: Agent-based semantic integration of information in open and dynamic environments. In Proceedings of SIGMOD’97, 1997.Google Scholar
  10. 10.
    C.A. Knoblock, Y. Arens, and C.-N. Hsu. Cooperating agents for information retrieval. In Proceedings of the 2nd International Conference on Cooperative Information Systems, Toronto, Canada, 1994. University of Toronto Press.Google Scholar
  11. 11.
    J. Kolodner. Case-Based Reasoning. Morgan Kaufmann Publisher, Inc., 1993.Google Scholar
  12. 12.
    J. Kolodner and D. Leake. a tutorial introduction ot case-based reasoning. In D. Leake, editor, Case-Based Reasoning:Experiences, lessons & Future Directions. American Association for Artificial Intelligence, 1996.Google Scholar
  13. 13.
    P. Koton. Reasoning about evidence in causal explanation. In Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), Cambridge, MA, 1988. AAAI Press/MIT Press.Google Scholar
  14. 14.
    V. Lesser, B. Horling, F. Klassner, A. Raja, and T. Wagner. Information gathering as a resource bounded interpretation task. Technical Report 97-34, University of Massachusetts Computer Science, March 1997.Google Scholar
  15. 15.
    S. Li and Q. Yang. ActiveCBR: Integrating case-based reasoning and active databases. Technical Report TR 1999-03, School of Computing Science, Simon Fraser University, Burnaby BC Canada, January 1999. http://www.cs.sfu.ca/qyang/Papers/activecbr.ps.gz.
  16. 16.
    H. Muñoz-Avila, D.C. McFarlane, D.W. Aha, L. Breslow, J.A. Ballas, and D. Nau. Using guidelines to constrain interactive case-based htn planning. Technical Report AIC-99-004, Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence, Washington, DC, 1999.Google Scholar
  17. 17.
    T. Nguyen, M. Czerwinski, and D. Lee. Compaq quicksource providing the consumer with the power of ai. AI Magazine, 1993.Google Scholar
  18. 18.
    A. Perini and F. Ricci. An interactive planning architecture: The forest fire fighting case. In M. Ghallab, editor, Proceedings of the 3rd European Workshop on Planning, pages 292–302, Assissi, Italy, September 1995. ISO Publishers.Google Scholar
  19. 19.
    J. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986.Google Scholar
  20. 20.
    M. Veloso, H. Munoz-Avila, and R. Bergmann. General-purpose case-based planning: Methods and systems. AI Communications, 9(3):128–137, 1996.Google Scholar
  21. 21.
    I. Watson. Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann Publishers, Inc., 1997.Google Scholar
  22. 22.
    Q. Yang. Formalizing planning knowledge for hierarchical planning. Computational Intelligence, 6, 1990.Google Scholar
  23. 23.
    Q. Yang, I. Abi-Zeid, and L. Lamontagne. An agent system for intelligent situation assessment. In F. Giunchiglia, editor, Proceedings of the 1998 International Conference on AI Methodologies, Systems and Applications (AIMSA98), volume 1480 of Lecture Notes in AI, pages 466–474, Sozopol, Bulgaria, September 1998. Springer Verlag.Google Scholar
  24. 24.
    Q. Yang, E. Kim, and K. Racine. Caseadvisor: Supporting interactive problem solving and case base maintenance for help desk applications. In Proceedings of the IJCAI 97 Workshop on Practical Applications of CBR, Nagoya, Japan, August 1997. International Joint Conference on Artificial Intelligence, IJCAI.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Christina Carrick
    • 1
  • Qiang Yang
    • 1
  • Irene Abi-Zeid
    • 2
  • Luc Lamontagne
    • 2
  1. 1.Simon Fraser UniversityBurnabyCanada
  2. 2.Defense Research Establishment ValcartierDecision Support TechnologyQuebecCanada

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