Abstract
Very often a planning problem can be formulated as a ranking problem: i.e. to find an order relation over a set of alternatives. The ranking of a finite set of alternatives can be designed as a preference elicitation problem. While the casebased preference elicitation approach is more effective with respect to the first principle methods, still the scaling problem remains an open issue because the elicitation effort has a quadratic relation with the number of alternative cases. In this paper we propose a solution based on the machine learning techniques. We illustrate how a boosting algorithm can effectively estimate pairwise preferences and reduce the effort of the elicitation process. Experimental results, both on artificial data and a realworld problem in the domain of civil defence, showed that a good trade-off can be achieved between the accuracy of the estimated preferences, and the elicitation effort of the end user.
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Avesani, P., Ferrari, S., Susi, A. (2003). Case-Based Ranking for Decision Support Systems. In: Ashley, K.D., Bridge, D.G. (eds) Case-Based Reasoning Research and Development. ICCBR 2003. Lecture Notes in Computer Science(), vol 2689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45006-8_6
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DOI: https://doi.org/10.1007/3-540-45006-8_6
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