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There are several situations where humans express their preferences in order to take good decisions. The major problem is that humans' preferences are more and more complex, the multiple criteria considered are often conflicting and the number of alternatives is too large to be explicitly handled. The objective of Multi-Criteria Decision Making (MCDM) approaches is to efficiently model and solve such complex decision problems. In this paper, we propose a framework allowing on one hand to encode users' preferences about the alternatives regarding the available criteria using a logic-based approach which is a variant of the Qualitative Choice Logic (QCL). On the other hand, the importance of each criterion is considered and computed in terms of probability degrees with respect to what is already known about the person who takes the decision. The available alternatives are then evaluated following two aspects: the first one concerns verifying if a given alternative is satisfied in the preferred models of the users' preferences while the second one is related to the criterion's importance.