Abstract
How to automate case adaptation is a classic problem for case-based reasoning. Given the difficulty of developing reliable case adaptation methods, it is appealing to consider methods which can exploit the strengths of a set of alternative adaptation methods. This paper presents a framework for combining suggestions from multiple adaptation methods, and illustrates and evaluates the approach in the context of interactive support for user modification of scientific workflows. The paper presents four adaptation methods for this domain, describes a method for assessing their confidence, proposes four strategies for suggestion combination, and evaluates the performance of the approach. The evaluation suggests that, for this domain, results depend more strongly on the adaptation methods chosen than on the specific combination method used, and that they depend especially strongly on a confidence threshold used for limiting irrelevant and incorrect suggestions.
This material is based on work supported by the National Science Foundation under Grant No. OCI-0721674. We thank Beth Plale and the Indiana University SDCI group at IU for their vital contributions to this work and the anonymous reviewers for their helpful comments.
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
Mantaras, R., McSherry, D., Bridge, D., Leake, D., Smyth, B., Craw, S., Faltings, B., Maher, M., Cox, M., Forbus, K., Keane, M., Aamodt, A., Watson, I.: Retrieval, reuse, revision, and retention in CBR. Knowledge Engineering Review 20(3) (2005)
Wilke, W., Vollrath, I., Althoff, K.D., Bergmann, R.: A framework for learning adaptation knowledge based on knowledge light approaches. In: Proceedings of the Fifth German Workshop on Case-Based Reasoning, pp. 235–242 (1997)
Leake, D., Kendall-Morwick, J.: Towards case-based support for e-science workflow generation by mining provenance information. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS, vol. 5239, pp. 269–283. Springer, Heidelberg (2008)
Patterson, D., Rooney, N., Galushka, M.: A regression based adaptation strategy for case-based reasoning. In: Proceedings of the Eighteenth Annual National Conference on Artificial Intelligence, pp. 87–92. AAAI Press, Menlo Park (2002)
Plaza, E., Ontañón, S.: Ensemble case-based reasoning: Collaboration policies for multiagent cooperative CBR. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS, vol. 2080, p. 437. Springer, Heidelberg (2001)
Yu, J., Buyya, R.: A taxonomy of scientific workflow systems for grid computing. SIGMOD Rec. 34(3), 44–49 (2005)
Xiang, X., Madey, G.R.: Improving the reuse of scientific workflows and their by-products. In: ICWS, pp. 792–799. IEEE Computer Society, Los Alamitos (2007)
Maechling, P., Chalupsky, H., Dougherty, M., Deelman, E., Gil, Y., Gullapalli, S., Gupta, V., Kesselman, C., Kim, J., Mehta, G., Mendenhall, B., Russ, T., Singh, G., Spraragen, M., Staples, G., Vahi, K.: Simplifying construction of complex workflows for non-expert users of the southern california earthquake center community modeling environment. SIGMOD Rec. 34(3), 24–30 (2005)
Freitag, D.: Machine Learning for Information Extraction in Informal Domains. PhD thesis, Carnegie Mellon University (1998)
Roure, D.D., Goble, C., Bhagat, J., Cruickshank, D., Goderis, A., Michaelides, D., Newman, D.: Myexperiment: Defining the social virtual research environment. In: 4th IEEE International Conference on e-Science (August 2008)
Craw, S., Wiratunga, N., Rowe, R.C.: Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence 170(16-17), 1175–1192 (2006)
Sooriamurthi, R.: Multi-case-base reasoning. PhD thesis, Indiana University (2007)
Sigletos, G., Paliouras, G., Spyropoulos, C.D., Hatzopoulos, M.: Combining information extraction systems using voting and stacked generalization. J. Mach. Learn. Res. 6, 1751–1782 (2005)
Dietterich, T.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
d’Aquin, M., Badra, F., Lafrogne, S., Lieber, J., Napoli, A., Szathmary, L.: Case base mining for adaptation knowledge acquisition. In: Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 750–755. Morgan Kaufmann, San Mateo (2007)
Leake, D., Kinley, A., Wilson, D.: Learning to improve case adaptation by introspective reasoning and CBR. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 229–240. Springer, Heidelberg (1995)
Cheetham, W., Price, J.: Measures of solution accuracy in case-based reasoning systems. In: Funk, P., González, P. (eds.) ECCBR 2004. LNCS, vol. 3155, pp. 106–118. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leake, D., Kendall-Morwick, J. (2009). Four Heads Are Better than One: Combining Suggestions for Case Adaptation. In: McGinty, L., Wilson, D.C. (eds) Case-Based Reasoning Research and Development. ICCBR 2009. Lecture Notes in Computer Science(), vol 5650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02998-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-02998-1_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02997-4
Online ISBN: 978-3-642-02998-1
eBook Packages: Computer ScienceComputer Science (R0)