Skip to main content
SpringerLink
Log in
Book cover

International Conference on Case-Based Reasoning

ICCBR 1997: Case-Based Reasoning Research and Development pp 489–498Cite as

Qualitative knowledge to support reasoning about cases

  • Scientific Papers
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1266))

Abstract

Our recipe planner for bioprocesses, Sophist, uses a semi-qualitative model to reason about cases. The model represents qualitative knowledge about the possible effects of differences between cases and about the possible causes of observed problems. Hence, the model is a crucial resource of adaptation knowledge. The model representation has been developed specifically to support CBR tasks. The essential notion in this representation is that of an influence. Representation of domain knowledge in an influence graph and a mapping of case-features onto nodes of such a graph, enable a variety of interesting reasoning tasks. Examples of such task illustrate how qualitative reasoning and case-based reasoning support each other in complex planning tasks.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Aamodt, A. (1994). Explanation-driven case-based reasoning, In S. Wess, K. Althoff, M. Richter (Eds.): Topics in Case-based reasoning. Springer Verlag, pp. 274–288.

    Google Scholar 

  • Aarts, R.J. & Rousu, J. (1996). Towards CBR for bioprocess planning. In Smith I., Faltings, B., (Eds.): Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial Intelligence, 1186: 16–27.

    Google Scholar 

  • Ashley, K.D. & Aleven, V. (1996). How different is different? Arguing About the Significance of Similarities and Differences. In Smith I., Faltings, B., (Eds.): Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial Intelligence, 1186: 1–15.

    Google Scholar 

  • Bhatta, S., Goel, A. & Prabhakar, S. (1994). Innovation in Analogical Design: A Model-Based Approach. Proc. of the Third International Conference on AI in Design, Aug. 1994, Lausanne, Switzerland.

    Google Scholar 

  • DeJong, G. F. (1994). Learning to plan in continuous domains. Artificial Intelligence 65: 71–141.

    Google Scholar 

  • Falkenhainer, B., Forbus, K.D. & Gentner, D. (1989). The Structure-Mapping Engine: Algorithm and Examples. Artificial Intelligence 41: 1–63.

    Google Scholar 

  • Forbus. K.D. (1984). Qualitative Process Theory. Artificial Intelligence 24: 85–168.

    Google Scholar 

  • Hammond, K. (1990). Explaining and Repairing Plans That Fail. Artificial Intelligence, 45:173–228.

    Google Scholar 

  • Hanney, K. & Keane, M.T. (1996). Learning Adaptation Rules from a Case-Base. In Smith I., Faltings, B., (Eds). Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial Intelligence, 1186: 179–192.

    Google Scholar 

  • Hastings, J.D., Branting, L.K. & Lockwood, J.A. (1995), Case Adaplalion Using an Incomplete Causal Model. In: Veloso, M. & Aamodl, A. (Eds.): Proceedings ICCBR-95, Sesimbra, Lecture Notes in Artificial Intelligence, 1010: 181–192.

    Google Scholar 

  • Leake, D.B., Kinley, A. & Wilson, D. Learning to Improve Case Adaptation by Introspective Reasoning and CBR. In: Veloso, M. & Aamodt, A. (Eds.): Proceedings ICCBR-95, Sesimbra, Lecture Notes in Artificial Intelligence, 1010: 229–240.

    Google Scholar 

  • Kamp, G. (1996). Using Description Logics for Knowledge Intensive Case-Based Reasoning. In Smith I., Faltings, B., (Eds). Proceedings of EWCBR-96, Lausanne, Lecture Notes in Artificial Intelligence, 1186: 204–218

    Google Scholar 

  • Koton, P. (1989). Using experience in learning and problem solving. Massachusetts Institute of Technology, Laboratory of Computer Science (Ph.D. diss., October 1988), MIT/LCS/TR-441.

    Google Scholar 

  • Nayak P. & Joskowicz, L. (1996). Efficient compositional modeling for generating causal explanations. Artificial Intelligence 83: 193–227.

    Google Scholar 

  • Richter, M. (1995). The similarity Issue in CBR: The knowledge contained in similarity measures, Invited talk at ICCBR-95, Sesimbra.

    Google Scholar 

  • Say, A.C.C. & Kuru, S. (1996). Qualitative system identification: deriving structure from behavior. Artificial Intelligence 83: 75–141.

    Google Scholar 

  • Schank, R.C. & Leake, D.B. (1989). Creativity and Learning in a Case-Based Explainer. Artificial Intelligence 40: 353–385.

    Google Scholar 

  • Sycara, K., Guttal, R., Koning, J., Narasimhan, S. & Navinchandra, D. (1992) CADET: a Case-based Synthesis Tool for Engineering Design, Intl. J. Expert Systems, 4:2.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. VTT Biotechnology and Food Research, P.O. Box 1500, FIN-02044, VTT, Finland

    Robert J. Aarts & Juho Rousu

Authors
  1. Robert J. Aarts
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juho Rousu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

David B. Leake Enric Plaza

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Aarts, R.J., Rousu, J. (1997). Qualitative knowledge to support reasoning about cases. In: Leake, D.B., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 1997. Lecture Notes in Computer Science, vol 1266. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63233-6_518

Download citation

  • DOI: https://doi.org/10.1007/3-540-63233-6_518

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63233-7

  • Online ISBN: 978-3-540-69238-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation