Abstract
Transformational analogy is currently more widely employed than derivational analogy in CBR applications, even though the latter has significant advantages over the former. The main reason for the reluctance to use derivational analogy is the complexity of representation. Other factors include issues related to retrieval and difficulties in system validation. Means of addressing these issues are described in this paper. Unique opportunities offered by the approach are illustrated with examples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carbonell, J.G.: Learning by analogy: formulating and generalizing plans from past experience. In: Michalski, Carbonell, Mitchell (eds.) Machine Learning, An artificial intelligence approach. Morgan Kaufmann, Boston (1983)
Carbonell, J.: Derivational analogy: A theory of reconstructive problem solving and expertise acquisition. In: Michalski, Carbonell, Mitchell (eds.) Machine Learning, An artificial intelligence approach, vol. 2. Morgan Kaufmann, Boston (1986)
Muñoz-Ávila, H., Ricci, F. (eds.): Case-Based Reasoning Research and Development. LNCS (LNAI), vol. 3620. Springer, Heidelberg (2005)
Funk, P., Calero, P.A.G. (eds.): ECCBR 2004. LNCS (LNAI), vol. 3155. Springer, Heidelberg (2004)
Kolodner, J.L.: Case-based Reasoning. Morgan Kaufmann, San Mateo (1993)
Veloso, M., Carbonell, J.: Derivational analogy in PRODIGY: Automating case acquisition, storage, and utilization. Machine Learning 10, 249–278 (1993)
Raphael, B.: Reconstructive Memory in Design Problem Solving, PhD thesis, University of Strathclyde, Glasgow, UK (1995)
Kumar, B., Raphael, B.: Derivational Analogy Based Structural Design. Saxe-Coburg Publications, UK (2002)
Raphael, B., Saitta, S.: Knowprice: using derivational analogy to estimate project costs. In: Topping, B.H.V. (ed.) Proceedings of the eighth international conference on the application of artificial intelligence to civil, structural and environmental engineering. Civil-Comp. Press (2005)
Cheng, Y., Melhem, H.G.: Monitoring bridge health using fuzzy case-based reasoning. Advanced Engineering Informatics 19(4) (2005)
Plaza, E.: Cooperative Reuse for Compositional Cases in Multi-agent Systems. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 382–396. Springer, Heidelberg (2005)
Cox, M.T., Veloso, M.: Supporting Combined Human and Machine Planning: An Interface for Planning by Analogical Reasoning. In: Leake, D.B., Plaza, E. (eds.) ICCBR 1997. LNCS, vol. 1266, pp. 531–540. Springer, Heidelberg (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raphael, B. (2006). Derivational Analogy: Challenges and Opportunities. In: Smith, I.F.C. (eds) Intelligent Computing in Engineering and Architecture. EG-ICE 2006. Lecture Notes in Computer Science(), vol 4200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11888598_49
Download citation
DOI: https://doi.org/10.1007/11888598_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46246-0
Online ISBN: 978-3-540-46247-7
eBook Packages: Computer ScienceComputer Science (R0)