Abstract

Usually, not all the solutions of a finite domain constraint problem (CSP) are equally desirable: some of them may be preferred to others. However, classical CSPs do not allow for this more informative kind of knowledge representation. On the other hand, semiring-based CSPs (SCSPs), where a value is associated with each tuple in each constraint, generate solutions with a corresponding value attached, which can be interpreted as the level of preference of that solution. Sometimes, however, even standard SCSPs are not enough, since one may know his/her preferences over some of the solutions but have no idea on how to code this knowledge into the SCSP. In this paper we consider this situation and propose to address it by first defining a classical CSP and giving some examples of solution preferences, and then learning the corresponding SCSP which behaves as the initial CSP (that is, it has the same solutions) and matches the preferences specified in the examples. In other words, we use th...

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