We consider soft constraint problems where some of the preferences may be unspecified. In practice, some preferences may be missing when there is, for example, a high cost for computing the preference values, or an incomplete elicitation process. Within such a setting, we study how to find an optimal solution without having to wait for all the preferences. In particular, we define a local search approach that interleaves search and preference elicitation, with the goal to find a solution which is “necessarily optimal”, that is, optimal no matter the missing data, whilst asking the user to reveal as few preferences as possible. Previously, this problem has been tackled with a systematic branch & bound algorithm. We now investigate whether a local search approach can find good quality solutions to such problems with fewer resources. While the approach is general, we evaluate it experimentally on a class of meeting scheduling problems with missing preferences. The experimental results sho...

A Local Search Approach for Incomplete Soft Constraint Problems: Experimental Results on Meeting Scheduling Problems

PINI, MARIA SILVIA;ROSSI, FRANCESCA;
2017

Abstract

We consider soft constraint problems where some of the preferences may be unspecified. In practice, some preferences may be missing when there is, for example, a high cost for computing the preference values, or an incomplete elicitation process. Within such a setting, we study how to find an optimal solution without having to wait for all the preferences. In particular, we define a local search approach that interleaves search and preference elicitation, with the goal to find a solution which is “necessarily optimal”, that is, optimal no matter the missing data, whilst asking the user to reveal as few preferences as possible. Previously, this problem has been tackled with a systematic branch & bound algorithm. We now investigate whether a local search approach can find good quality solutions to such problems with fewer resources. While the approach is general, we evaluate it experimentally on a class of meeting scheduling problems with missing preferences. The experimental results sho...
2017
Proceedings of 14th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR 2017)
14th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming, CPAIOR 2017
9783319597751
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3233390
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