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Applying memory-based learning to indexing of reference ships for case-based conceptual ship design

  • Dongkon Lee
  • Jaeho Kang
  • Kwang Ryel Ryu
  • Kyung-Ho Lee
Application Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1266)

Abstract

This paper presents a method of applying a memory-based learning (MBL) technique to automatic building of an indexing scheme for accessing reference cases during the conceptual design phase of a new ship. The conceptual ship design process begins with selecting previously designed reference ships of the same type with similar sizes and speeds. These reference ships are used for deriving an initial design of a new ship, and then the initial design is kept modified and repaired until the design reaches a level of satisfactory quality. The selection of good reference ships is essential for deriving a good initial design, and the quality of the initial design affects the efficiency and quality of the whole conceptual design process. The selection of reference ships has so far been done by design experts relying on their experience and engineering knowledge of ship design and structural mechanics. We developed an MBL method that can build an effective indexing scheme for retrieving good reference cases from a case base of previous ship designs. Empirical results show that the indexing scheme generated by MBL outperforms those by other learning methods such as the decision tree learning.

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References

  1. Aha W. D., (1991) Case-Based Learning Algorithms, Proceedings of the 1991 DARPA Case-Based Reasoning Workshop, pp. 147–158, Morgan Kaufmann.Google Scholar
  2. Aha W. D., (1997) Editorial on Lazy Laerning, to appear in Artificial Intelligence Review Special Issue on Lazy Learning.Google Scholar
  3. Andrews D., (1981) Creative Ship Design, The Royal Institution of Naval Architects, Nov., 1981, pp. 447–471.Google Scholar
  4. Atekson C., A. Moore and S. Schaal, (1997) Locally Weighted Learning, to appear in Artificial Intelligence Review Special Issue on Lazy Learning.Google Scholar
  5. Cost S. and S. Salzberg, (1993) A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features, Machine Learning, 10, pp. 57–78.Google Scholar
  6. Cover, T. and P. Hart, (1967) Nearest Neighbor Pattern Classification, IEEE Transactions on Information Theory, 13, pp. 21–27.Google Scholar
  7. Friedman, J.H., R. Kohavi, and Y. Yun, Lazy Decision Trees, (1996) Proceedings of the Thirteenth National Conference on Artificial Intelligence, Portland, Oregon.Google Scholar
  8. Ricci F. and P. Avesani, (1995) Learning a Local Similarity Metric for Case-Based Reasoning, Preceedings of the First International Conference on Case-Based Reasoning (ICCBR-95), pp. 23–26, Sesimbra, Portugal.Google Scholar
  9. Schwabacher M., H. Hirsh and T. Ellman, (1994) Learning Prototype-Selection Rules for Case-Based Iterative Design, Proceedings of the Tenth IEEE Conference on Artificial Intelligence for Applications, San Antonio, Texas.Google Scholar
  10. Stanfill C. and D. Waltz, (1986) Toward Memory-Based Reasoning, Communication of ACM, 29, pp. 1213–1229.Google Scholar
  11. Quinlan J. R., (1993) C4.5:Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Dongkon Lee
    • 1
  • Jaeho Kang
    • 2
  • Kwang Ryel Ryu
    • 2
  • Kyung-Ho Lee
    • 1
  1. 1.Shipbuilding System DepartmentKorean Research Institute of Ships and Ocean EngineeringYusung-Ku, TeajeonKorea
  2. 2.Department of Computer EngineeringPusan National UniversityKumjeong-Ku, PusanKorea

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