Abstract
Case-Based Design (CBD) applies a knowledge-based process to the knowledge commonly associated with Case-Based Reasoning (CBR) systems — the library of exemplars. This paper investigates the problems in using commercial CBR tools, primarily aimed at classification applications, for a more knowledge intensive CBD task, and proposes techniques that overcome some of these difficulties. This work results from the development of a pharmaceutical CBD system Cbr-Tfs that proposes tablet formulations in order to manufacture viable tablets. Results show that Cbr-Tfs proposes useful ingredients for the tablet, and that the quantities it suggests are well within the limits of the tablet manufacturing process. CBD's increased need for specialised adaptation knowledge is also highlighted and this raises the issue of its acquisition.
The work reported here underpins a recently awarded EPSRC grant (GR/L98015) that aims to provide automated tools that assist knowledge acquisition for CBD.
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References
A. Aamodt and E. Plaza. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AICOM, 7(1):39–59, 1994.
S. R. Bhatta and A. K. Goel. Model-based design indexing and index learning in engineering design. Engineering Applications of Artificial Intelligence, 9(6):601–609, 1996.
S. Craw, R. Boswell, and R. Rowe. Knowledge refinement to debug and maintain a tablet formulation system. In Proceedings of the 9TH IEEE International Conference on Tools with Artificial Intelligence (TAI'97), pages 446–453, Newport Beach, CA, 1997. IEEE Press.
J. Frank, B. Rupprecht, and W. Schmelmer. Knowledge-based assistance for the development of drugs. IEEE Expert, 12(1):40–48, 1997.
F. Gebhardt, A. Vo\, W. Gräther, and B. Schmidt-Belz. Reasoning with Complex Cases. Kluwer, 1997.
K. J. Hammond. Explaining and repairing plans that fail. AI, 45(1–2):173–228, 1990.
T. Hinrichs and J. Kolodner. The roles of adaptation in case-based design. In Proceedings of the Ninth National Conference on Artificial Intelligence, Cambridge, MA, 1991. AAAI Press/MIT Press.
D. B. Leake. Combining rules and cases to learn case adaptation. In Proceedings of the 17th Annual Conference of the Cognitive Science Society, pages 84–89, Pittsburgh, PA, 1995.
M. L. Maher, M. B. Balachandran, and D. M. Zhang. Case-Based Reasoning in Design. Lawrence Erlbaum, Mahwah, NJ, 1995.
M. Pearce, A. K. Goel, J. L. Kolodner, and others. Case-based design support: A case in architectural design. IEEE Expert, 7(5):14–20, October 1992.
B. Porter, R. Bareiss, and R. C. Holte. Concept learning and heuristic classification in weak-theory domains. AI, 45(3):229–263, 1990.
M. M. Richter. The knowledge contained in similarity measures. Invited Talk at the 1st International Conference on CBR (http: //wwwagr.informatik.unikl.de/~lsa/CBR/Richtericcbr95remarks.html), 1995.
R. Rowe. An expert system for the formulation of pharmaceutical tablets. Manufacturing Intelligence, 14:13–15, 1993.
J. Surma and B. Braunschweig. Case-base retrieval in process engineering: Supporting design by reusing flowsheets. Engineering Applications of Artificial Intelligence, 9(4):385–391, 1996.
D. Wettschereck and D. W. Aha, editors. Proceedings of the ECML-97 Workshop on Case-Based Learning: Beyond Classification of Feature Vectors, 1997.
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© 1998 Springer-Verlag Berlin Heidelberg
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Craw, S., Wiratunga, N., Rowe, R. (1998). Case-Based design for tablet formulation. In: Smyth, B., Cunningham, P. (eds) Advances in Case-Based Reasoning. EWCBR 1998. Lecture Notes in Computer Science, vol 1488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056347
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DOI: https://doi.org/10.1007/BFb0056347
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