We consider configuration problems with preferences rather than just hard constraints, and we analyze and discuss the features
that such configurators should have. In particular, these configurators should provide explanations for the current state,
implications of a future choice, and also information about the quality of future solutions, all with the aim of guiding the
user in the process of making the right choices to obtain a good solution.
We then describe our implemented system, which, by taking the soft n-queens problem as an example, shows that it is indeed possible, even in this very general context of preference-based configurators,
to automatically compute all the information needed for the desired features. This is done by keeping track of the inferences
that are made during the constraint propagation enforcing phases.