Abstract
In preference-based CBR (Pref-CBR), problem solving experience is represented in the form of contextualized preferences, namely, preferences between candidate solutions in the context of a target problem to be solved. Since a potentially large number of such preferences can be collected in the course of each problem solving episode, case base maintenance clearly becomes an issue in Pref-CBR. In this paper, we therefore extend our Pref-CBR framework by another component, namely, a method for dynamic case base maintenance. The main goal of this method is to increase efficiency of case-based problem solving, by reducing the size of the case base, while maintaining performance. To illustrate the effectiveness of our approach, we present a case study in which Pref-CBR is used for the repetitive traveling salesman problem.
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Notes
- 1.
The solution \({\varvec{y}}^*\) could be a purely imaginary solution, which may not exist in practice.
- 2.
The notion of “direction” should not be taken literally. In fact, the mathematical structure of \(\mathbb {Y}\) will normally not allow for defining a direction in a geometrical sense.
- 3.
Actually, we could even create more than a qualitative preference, because the numerical values of the solutions (lengths of the tours) are known as well. This is indeed additional information we are not exploiting in this application.
- 4.
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Abdel-Aziz, A., Hüllermeier, E. (2015). Case Base Maintenance in Preference-Based CBR. In: Hüllermeier, E., Minor, M. (eds) Case-Based Reasoning Research and Development. ICCBR 2015. Lecture Notes in Computer Science(), vol 9343. Springer, Cham. https://doi.org/10.1007/978-3-319-24586-7_1
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