  Many real life optimization problems contain both hard and soft constraints,
as well as qualitative conditional preferences. However, there is no single
formalism to specify all three kinds of information. We therefore propose a
framework, based on both CP-nets and soft constraints, that handles both hard
and soft constraints as well as conditional preferences efficiently and
uniformly. We study the complexity of testing the consistency of preference
statements, and show how soft constraints can faithfully approximate the
semantics of conditional preference statements whilst improving the
computational complexity
