Skip to main content

A utility-based approach to learning in a mixed case-based and model-based reasoning architecture

  • Scientific Papers
  • Conference paper
  • First Online:

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

Abstract

Case-based reasoning (CBR) can be used as a form of “caching” solved problems to speedup later problem solving. Using “cached” cases brings additional costs with it due to retrieval time, case adaptation time and also storage space. Simply storing all cases will result in a situation in which retrieving and trying to adapt old cases will take more time (on average) than not caching at all. This means that caching must be applied selectively to build a case memory that is actually useful. This is a form of the utility problem [4, 2]. The approach taken here is to construct a “cost model” of a system that can be used to predict the effect of changes to the system. In this paper we describe the utility problem associated with “caching” cases and the construction of a “cost model”. We present experimental results that demonstrate that the model can be used to predict the effect of certain changes to the case memory.

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

  1. Console, L., Portinale, L., Theseider, D., and Torasso, P. (1993). Combining Heuristic Reasoning with Causal Reasoning In Diagnostic Problem Solving, pages 46–68. Springer Verlag.

    Google Scholar 

  2. Francis, A. G. and Ram, A. (1995). A comparative utility analysis of case-based reasoning and controle-rule learning systems. In Lavrac, N. and Wrobel, S., editors, Machine Learning: ECML-95, pages 138–150. Springer Verlag.

    Google Scholar 

  3. Keller, R. M. (1988). Defining operationality for explanation-based learning. Artificial Intelligence, 35:227–241.

    Google Scholar 

  4. Minton, S. (1988). Learning Effective Search Control Knowledge: An Explanation-Based Approach. Kluwer.

    Google Scholar 

  5. Portinale, L. and Torasso, P. (1995). Adapter: an integrated diagnostic system combining case-based and abductive reasoning. In Veloso, M. and Aamodt, A., editors, Proceedings ICCBR-95, pages 277–288. Springer Verlag.

    Google Scholar 

  6. Portinale, L. and Torasso, P. (1996). On the usefulness of re-using diagnostic solutions. In Wahlster, W., editor, Proceedings 12th European Conference on Artificial Intelligence ECAI-96, pages 137–141. John Wiley and Sons.

    Google Scholar 

  7. Smyth, B. and Keane, M.(1995). Remembering to forget. In Mellish, C., editor, Proceedings IJCAI-95, pages 377–382. Morgan Kaufmann.

    Google Scholar 

  8. Straatman, R. and Beys, P. (1995). A performance model for knowledge-based systems. In Ayel, M. and Rousse, M. C., editors, EUROVAV-95 European Symposium on the Validation and Verification of Knowledge Based Systems, pages 253–263. ADEIRAS, Université de Savoie, Chambéry.

    Google Scholar 

  9. Subramanian, D. and Hunter, S. (1992). Measuring utility and the design of provably good ebl algorithms. In Sleeman, D. and Edwards, P., editors, Machine Learning: Proceedings of the Ninth International Workshop ML-95, pages 426–435. Morgan Kaufmann.

    Google Scholar 

  10. van Harmelen, F. (1994). A model of costs and benefits of meta-level computation. In Proceedings of the Fourth Workshop on Meta-programming in Logic (META '94), volume 883 of LNCS, pages 248–261. Springer-Verlag.

    Google Scholar 

  11. van Someren, M. W., Zheng, L. L., and Post, W. (1990). Cases, models or compiled knowledge? — a comparative analysis and proposed integration. In Wielinga, B. J., Boose, J., Gaines, B., Schreiber, G., and van Someren, M. W., editors, Current trends in knowledge acquisition, pages 339–355. IOS Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

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

van Someren, M., Surma, J., Torasso, P. (1997). A utility-based approach to learning in a mixed case-based and model-based reasoning architecture. 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_517

Download citation

  • DOI: https://doi.org/10.1007/3-540-63233-6_517

  • 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