Cases, information, and agents | SpringerLink
Skip to main content
Springer Nature Link
Log in

Cases, information, and agents

  • Invited Papers
  • Conference paper
  • First Online:
Cooperative Information Agents (CIA 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1202))

Included in the following conference series:

Abstract

Case Retrieval Nets (CRNs) have been developed for the fast and flexible retrieval of previous cases (“experiences”) from large case bases. They permit the ranking of stored information according to their similarity to a query.

As an effective flexible information gathering technique they are appropriate for building information agents working over inhomogeneous data bases, too.

CRNs allow the addition and/or removal of information and indexes even at runtime, which makes them potentially useful for the adaptation and self-organization of information agents in changing environments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. R. Bartlett. Remembering: A study in experimental and social psychology. Cambridge University Press, London, 1932.

    Google Scholar 

  2. H.-D. Burkhard. Case retrieval nets. Techn. report, Humboldt University, Berlin, 1995.

    Google Scholar 

  3. H.D. Burkhard and M. Lenz. Case retrieval nets: Basic ideas and extensions. In 4th German Workshop on CBR, eds., H.D. Burkhard and M.Lenz, pp. 103–110. Humboldt University Berlin, (1996).

    Google Scholar 

  4. H.D. Burkhard, G. Lindemann, S.A. Loening, and J. Neymeyer. Remembering the Unexpected Cases — CBR for Experts in Urology. Proc. ECAI-96-WS “Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-96)”, 11–14.

    Google Scholar 

  5. K. Goos. Preselection strategies for case based classification. In B.Nebel, and Dreschler-Fischer,L., eds.: KI-94: Advances in Case-Based Reasoning. Springer, 1994.

    Google Scholar 

  6. G. Kamp, P. Pirk, and H.D. Burkhard. Falldaten: Case-based Reasoning for the Diagnosis of Technical Devices. KI-96: Advances in Artificial Intelligence. (Proc. 20th Annual German Conference on AI.) LNAI 1137, pp. 149–161 (1996).

    Google Scholar 

  7. J. L. Kolodner. Case-Based Reasoning. Morgan Kaufmann, San Mateo, 1993.

    Google Scholar 

  8. 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 

  9. M. Lenz, H. Ladewig. Fallbasierte Unterstützung bei der Immobilienbewertung. Wirtschaftsinformatik 38(1), Schwerpunktheft “Fallbasierte Entscheidungsunterstützung”, 1996.

    Google Scholar 

  10. M. Lenz. Case Retrieval Nets applied to large case bases. In H.-D. Burkhard, B. Bartsch-Spörl, D. Janetzko, and S. Weß, editors, 4th German Workshop on CBR — System Development and Evaluation, Berlin, 1996. Humboldt University.

    Google Scholar 

  11. M. Lenz and H.D. Burkhard. ‘Lazy propagation in case retrieval nets'. In 12th ECAI 1996, ed., W. Wahlster, pp. 127–131. John Wiley & Sons, (1996).

    Google Scholar 

  12. M. Lenz and H.D. Burkhard. Case Retrieval Nets: Basic Ideas and Extensions. KI-96: Advances in Artificial Intelligence. (Proc. 20th Annual German Conference on AI.) LNAI 1137, pp. 227–239 (1996).

    Google Scholar 

  13. M. Lenz and H.-D. Burkhard. Case retrieval nets: Foundations, properties, implementation, and results. Techn. report, Humboldt University, Berlin, 1996.

    Google Scholar 

  14. M. Lenz, H.D. Burkhard, and S. Brückner. Applying Case Retrieval Nets to Diagnostic Tasks in Technical Domains. I. Smith, B. Faltings (Eds.): Advances in Case-Based Reasoning. Proc. of the Third European Workshop EWCBR-96. Lecture Notes in Artificial Intelligence, 1168, Springer, pp. 219–233 (1996).

    Google Scholar 

  15. M. Lenz, E. Auriol, H.D. Burkhard, M. Manago, and P. Pirk. CBR for Diagnosis and Decision Support. AI Communications 9 (1996) 1–9.

    Google Scholar 

  16. A. S. Rao and M. P. Georgeff. Modeling agents within a BDI-architecture. Proc. of the First Int. Conf. on Multi-Agent Systems (ICMAS-95)), ed., V. Lesser, pp. 312–319. MIT-Press, (1995).

    Google Scholar 

  17. R. Schank. Dynamic memory: A theory of learning in computers and people. Cambridge Unv. Press, New York, 1982.

    Google Scholar 

  18. S. Weß. Fallbasiertes Problemlösen in wissensbasierten Systemen zur Entscheidungsunterstützung und Diagnostik. PhD thesis, Universität Kaiserslautern, 1995.

    Google Scholar 

  19. D. Wieczorek. ELIAS — Ein persönLicher Internet ASsistent. Diploma thesis, Humboldt University Berlin (in preparation).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Inst. of Informatics, Humboldt University Berlin, 10099, Berlin, Germany

    Hans-Dieter Burkhard

Authors
  1. Hans-Dieter Burkhard
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Peter Kandzia Matthias Klusch

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Burkhard, HD. (1997). Cases, information, and agents. In: Kandzia, P., Klusch, M. (eds) Cooperative Information Agents. CIA 1997. Lecture Notes in Computer Science, vol 1202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62591-7_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-62591-7_24

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62591-9

  • Online ISBN: 978-3-540-68321-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation