Abstract
Beliefs, preferences and constraints occur together in many real world problems. However, reasoning with such intertwinement is rather unexplored in the AI literature. In this paper, we introduce a model whereby agents seek for decisions that satisfy their preferences based on their beliefs subject to certain constraints by extending the soft constraints framework to the belief function theory. Constraint-based solving machinery are then adapted for solving such kind of problems. A specific branch and bound algorithm is introduced.