Learning preference relations over combinatorial domains
نویسندگان
چکیده
We address the problem of learning preference relations over multiattribute (or combinatorial) domains. We do so by making hypotheses about the dependence structure between attributes that the preference relation enjoys. The first hypothesis we consider is the simplest one, namely, separability (no dependences between attributes: the preference over the values of each attribute is independent of the values of other attributes); then we consider the more general case where the dependence structure takes the form of an acyclic graph. In all cases, what we want to learn is a set of local preference relations (or equivalently, a CPnet) rather than a fully specified preference relation. We consider three forms of consistency between a CP-net and a set of examples, and for two of them we give an exact characterization in the case of separability, as well as complexity results.
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