Acquiring both constraint and solution preferences in interactive constraint systems

Constraints are useful to model many real-life problems. Soft constraints are even more useful, since they allow for the use of preferences, which are very convenient in many real-life problems. In fact, most problems cannot be precisely defined by using hard constraints only. However, soft constraint solvers usually can only take as input preferences over constraints, or variables, or tuples of domain values. On the other hand, it is sometimes easier for a user to state preferences over entire solutions of the problem. In this paper, we define an interactive framework where it is possible to state preferences both over constraints and over solutions, and we propose a way to build a system with such features by pairing a soft constraint solver and a learning module, which learns preferences over constraints from preferences over solutions. We also describe a working system which fits our framework, and uses a fuzzy constraint solver and a suitable learning module to search a catalog for the best products that match the user''s requirements.