Abstract
Preference-based CBR is conceived as a case-based reasoning methodology in which problem solving experience is mainly represented in the form of contextualized preferences, namely preferences for candidate solutions in the context of a target problem to be solved. This paper is a continuation of recent work on a formalization of preference-based CBR that was focused on an essential part of the methodology: a method to predict a most plausible candidate solution given a set of preferences on other solutions, deemed relevant for the problem at hand. Here, we go one step further by embedding this method in a more general search-based problem solving framework. In this framework, case-based problem solving is formalized as a search process, in which a solution space is traversed through the application of adaptation operators, and the choice of these operators is guided by case-based preferences. The effectiveness of this approach is illustrated in two case studies, one from the field of bioinformatics and the other one related to the computer cooking domain.
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
Hüllermeier, E., Schlegel, P.: Preference-based CBR: First steps toward a methodological framework. In: Ram, A., Wiratunga, N. (eds.) ICCBR 2011. LNCS, vol. 6880, pp. 77–91. Springer, Heidelberg (2011)
Doyle, J.: Prospects for preferences. Comput. Intell. 20(2), 111–136 (2004)
Goldsmith, J., Junker, U.: Special issue on preference handling for Artificial Intelligence. Computational Intelligence 29(4) (2008)
Domshlak, C., Hüllermeier, E., Kaci, S., Prade, H.: Preferences in AI: An overview. Artificial Intelligence (2011)
Brafman, R.I., Domshlak, C.: Preference handling–an introductory tutorial. AI Magazine 30(1) (2009)
Peterson, M.: An Introduction to Decision Theory. Cambridge Univ. Press (2009)
Kraay, D.R., Harker, P.T.: Case-based reasoning for repetitive combinatorial optimization problems, part I: Framework. Journal of Heuristics 2, 55–85 (1996)
Grolimund, S., Ganascia, J.G.: Driving tabu search with case-based reasoning. European Journal of Operational Research 103(2), 326–338 (1997)
Hüllermeier, E.: Focusing search by using problem solving experience. In: Horn, W. (ed.) Proceedings ECAI 2000, 14th European Conference on Artificial Intelligence, Berlin, Germany, pp. 55–59. IOS Press (2000)
Bergmann, R., Wilke, W.: Towards a new formal model of transformational adaptation in case-based reasoning. In: Prade, H. (ed.) ECAI 1998, 13th European Conference on Artificial Intelligence, pp. 53–57 (1998)
Karaman, M.W., et al.: A quantitative analysis of kinase inhibitor selectivity. Nature Biotechnology 26, 127–132 (2008)
Schmitt, S., Kuhn, D., Klebe, G.: A new method to detect related function among proteins independent of sequence and fold homology. Journal of Molecular Biology 323(2), 387–406 (2002)
Stock, M.: Learning pairwise relations in bioinformatics: Three case studies. Master’s thesis, University of Ghent (2012)
Ghahramani, Z., Heller, K.A.: Bayesian sets. In: Proceedings NIPS 2005 (2005)
Cheng, W., Hüllermeier, E.: Learning similarity functions from qualitative feedback. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 120–134. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abdel-Aziz, A., Cheng, W., Strickert, M., Hüllermeier, E. (2013). Preference-Based CBR: A Search-Based Problem Solving Framework. In: Delany, S.J., Ontañón, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2013. Lecture Notes in Computer Science(), vol 7969. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39056-2_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-39056-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39055-5
Online ISBN: 978-3-642-39056-2
eBook Packages: Computer ScienceComputer Science (R0)