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Acquiring Knowledge and Numerical Data to Support CBR Retrieval

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2473))

Abstract

This paper illustrates a Knowledge Acquisition and Representation tool (KARM) to support the design in a restricted and specific domain (i.e. the design of tyre treads for motor racing). The main goal of the KARM tool is to allow the acquisition and representation of track knowledge in terms of its morphology, and meteorological features. Two ways to analyze the geometric structure of tracks are combined into KARM; the first one is based on the acquisition and representation of qualitative knowledge (the block view technique), while the second one is based on the analysis of quantitative knowledge (P-Race Telemetry). Moreover, two fuzzy based modules that allow the representation of uncertain and imprecise knowledge about weather and asphalt conditions will be pointed out.

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References

  1. Akkermans, H., de Hoog, R., Shreiber, A., van de Velde, W., Wielinga, B., CommonKADS: A Comprehensive Methodology for KBS Development, IEEE Expert, pp 28–37, 1994.

    Google Scholar 

  2. Angele, J., Fensel, D., Studer, R., Developing Knowledge-Based Systems with MIKE, Journal of Automated Software Engineering, 1998.

    Google Scholar 

  3. Bandini, S., Manzoni, S., A Knowledge-Based System for the Design of Rubber Compounds in Motor Racing, Proceedings of 14th European Conference on Artificial Intelligence (ECAI) 2000, W. Horn (ed.), IOS Press, Amsterdam, 2000.

    Google Scholar 

  4. Bandini, S., Manzoni, S. CBR Adaptation for Chemical Formulation, in Aha, D.W., Watson, I. & Yang, Q. (Eds.), Proceedings of the 4th International Conference on Case Based Reasoning (ICCBR01), Case-Based Reasoning Research and Development, LNCS/LNAI 2080, Springer Verlag, 2001.

    Chapter  Google Scholar 

  5. Bandini, S., Manzoni, S., Application of Fuzzy Indexing and Retrieval in Case Based Reasoning for Design, Proceedings of the 2001 ACM Symposium on Applied Computing (SAC), March 11–14, 2001, Las Vegas, NV, USA, ACM, 2001, pp 462–466.

    Chapter  Google Scholar 

  6. Bonissone, P. P., Cheetham, W., Financial Application of Fuzzy Case-Based Reasoning to Residential Property Valuation, Proceedings of the 6th IEEE International Conference on Fuzzy Systems, Vol. 1, pp 37–44, 1997.

    Article  Google Scholar 

  7. Cairó, O., The KAMET Methodology: Contents, Usage and Knowledge Modeling, in Gaines, B. and Mussen, M. (eds.), Proceedings of the 11th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop (KAW’98), SRGD Publications, Department of Computer Science, University of Calgary, Proc-1, pp 1–20, 1998.

    Google Scholar 

  8. Gomide, F., Nakamiti, G., Fuzzy Sets in Distributed Traffic Control, 5th IEEE International Conference on Fuzzy Systems-FUZZ-IEEE 96, pp 1617–1623, New Orleans-LA-EUA, 1996

    Google Scholar 

  9. Hansen, B.K., Riordan, D., Weather Prediction Using Case-Based Reasoning and Fuzzy Set Theory, Workshop on Soft Computing in Case-Based Reasoning, 4th International Conference on Case-Based Reasoning (ICCBR01), Vancouver, 2001.

    Google Scholar 

  10. Hayes-Roth, F., Jacobstein, N., The State of Knowledge Based Systems, Communications of the ACM, 37(3) March 1994, pp 27–39.

    Google Scholar 

  11. Kolodner, J. Case-Based Reasoning, Morgan Kaufmann, San Mateo (CA), 1993.

    Google Scholar 

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© 2002 Springer-Verlag Berlin Heidelberg

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Bandini, S., Manzoni, S., Sartori, F. (2002). Acquiring Knowledge and Numerical Data to Support CBR Retrieval. In: Gómez-Pérez, A., Benjamins, V.R. (eds) Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web. EKAW 2002. Lecture Notes in Computer Science(), vol 2473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45810-7_2

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  • DOI: https://doi.org/10.1007/3-540-45810-7_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44268-4

  • Online ISBN: 978-3-540-45810-4

  • eBook Packages: Springer Book Archive

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