Answer set programming is a declarative programming paradigm based on logic programs under stable model semantics, respectively its generalization to answer set semantics. Besides the availability of rather efficient answer set solvers, one of the major reasons for the success of answer set programming in recent years was the shift from a theorem proving to a constraint programming view: problems are represented such that stable models, respectively answer sets, rather than theorems, correspond to solutions.

We believe that going one step further from a ``hard'' to a ``soft'' constraint programming paradigm, or, in other words, to a paradigm of qualitative optimization, will prove equally fruitful. In this talk we support this claim by showing that several generic problems in logic based problem solving can be understood as qualitative optimization problems, and that these problems have simple and elegant formulations given adequate optimization constructs in the knowledge representation language.