Advertisement

Applying case retrieval nets to diagnostic tasks in technical domains

  • Mario Lenz
  • Hans-Dieter Burkhard
  • Sven Brückner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1168)

Abstract

This paper presents Objectdirected Case Retrieval Nets, a memory model developed for an application of Case-Based Reasoning to the task of technical diagnosis. The key idea is to store cases, i.e. observed symptoms and diagnoses, in a network and to enhance this network with an object model encoding knowledge about the devices in the application domain.

Keywords

Technical diagnosis case retrieval memory structures 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    E. Auriol, S. Weß, M. Manago, K.-D. Althoff, and R. Traphöner. Inreca: A seamlessly integrated system based on inductive inference and case-based reasoning. In M. M. Veloso and A. Aamodt, editors, Case-Based Reasoning Research and Development (Proceedings of the First International Conference on CBR, ICCBR-95), Lecture Notes in Artificial Intelligence 1010, pages 371–380. Springer Verlag, 1995.Google Scholar
  2. 2.
    R. Bergmann, W. Wilke, I. Vollrath, and S. Weß. Integrating general knowledge with object-oriented case representation and reasoning. In H.-D. Burkhard and M. Lenz, editors, 4th German Workshop on CBR — System Development and Evaluation, pages 120–127, Berlin, 1996. Humboldt University.Google Scholar
  3. 3.
    M. G. Brown. A Memory Model for Case Retrieval by Activation Passing. PhD thesis, University of Manchester, 1994.Google Scholar
  4. 4.
    M. G. Brown. An underlying memory model to support case retrieval. In S. Weß, K.-D. Althoff, and M. M. Richter, editors, Topics in Case-Based Reasoning, Proceedings EWCBR-93, pages 132–143. Springer Verlag, 1994.Google Scholar
  5. 5.
    H.-D. Burkhard and M. Lenz. Case Retrieval Nets: Basic ideas and extensions. In H.-D. Burkhard and M. Lenz, editors, 4th German Workshop on CBR — System Development and Evaluation, — pages 103–110, Berlin, 1996. Humboldt University.Google Scholar
  6. 6.
    H.-D. Burkhard and P. Pirk. Technical diagnosis: Fallexperte-D. In H.-D. Burkhard and M. Lenz, editors, 4th German Workshop on CBR — System Development and Evaluation —, Berlin, 1996. Humboldt University.Google Scholar
  7. 7.
    M. Lenz. Case-based reasoning for holiday planning. In W. Schertler, B. Schmid, A. M. Tjoa, and H. Werthner, editors, Information and Communications Technologies in Tourism, pages 126–132. Springer Verlag, 1994.Google Scholar
  8. 8.
    M. Lenz, E. Auriol, H.-D. Burkhard, M. Manago, and P. Pirk. CBR für Diagnose und Entscheidungsunterstützung. Künstliche Intelligenz, Themenheft Fallbasiertes Schließen, 10(1):16–21, 1996.Google Scholar
  9. 9.
    M. Lenz and H.-D. Burkhard. Case Retrieval Nets: Basic ideas and extensions. In accepted for: KI-96, 1996.Google Scholar
  10. 10.
    M. Lenz and H.-D. Burkhard. Lazy propagation in Case Retrieval Nets. In W. Wahlster, editor, Proceedings 12th European Conference On Artificial Intelligence, pages 127–131, Los Angeles, 1996. John Wiley and Sons.Google Scholar
  11. 11.
    E. L. Rissland, D. B. Skalak, and M. T. Friedman. Case retrieval through multiple indexing and heuristic search. In Proceedings 13th International Joint Conference On Artificial Intelligence, pages 902–908, 1993.Google Scholar
  12. 12.
    J. W. Schaaf. ”Fish and Sink“: An anytime-algorithm to retrieve adequate cases. In M. M. Veloso and A. Aamodt, editors, Case-Based Reasoning Research and Development (Proceedings of the First International Conference on CBR, ICCBR-95), Lecture Notes in Artificial Intelligence 1010, pages 538–547. Springer Verlag, 1995.Google Scholar
  13. 13.
    A. Tversky. Features of similarity. Psychological Review, 84:327–352, 1977.Google Scholar
  14. 14.
    S. Weß. Fallbasiertes Problemlösen in wissensbasierten Systemen zur Entscheidungsunterstützung und Diagnostik. PhD thesis, Universität Kaiserslautern, 1995.Google Scholar
  15. 15.
    S. Weß, K.-D. Althoff, and G. Derwand. Using kd-trees to improve the retrieval step in case-based reasoning. In S. Weß, K.-D. Althoff, and M. M. Richter, editors, Topics in Case-Based Reasoning, Proceedings EWCBR-93, pages 167–181. Springer Verlag, 1994.Google Scholar
  16. 16.
    M. Wolverton. An investigation of marker-passing algorithms for analogue retrieval. In M. M. Veloso and A. Aamodt, editors, Case-Based Reasoning Research and Development (Proceedings of the First International Conference on CBR, ICCBR-95), Lecture Notes in Artificial Intelligence 1010, pages 359–370. Springer Verlag, 1995.Google Scholar
  17. 17.
    M. Wolverton and B. Hayes-Roth. Retrieving semantically distant analogies with knowledge-directed spreading activation. In Proceedings AAAI-94, 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Mario Lenz
    • 1
  • Hans-Dieter Burkhard
    • 1
  • Sven Brückner
    • 1
    • 2
  1. 1.Dept. of Computer ScienceHumboldt University BerlinBerlin
  2. 2.P.S.I. AGBerlin

Personalised recommendations