  Qualitative possibilistic networks, also known as min-based possibilistic
networks, are important tools for handling uncertain information in the
possibility theory frame- work. Despite their importance, only the junction
tree adaptation has been proposed for exact reasoning with such networks. This
paper explores alternative algorithms using compilation techniques. We first
propose possibilistic adaptations of standard compilation-based probabilistic
methods. Then, we develop a new, purely possibilistic, method based on the
transformation of the initial network into a possibilistic base. A comparative
study shows that this latter performs better than the possibilistic adap-
tations of probabilistic methods. This result is also confirmed by experimental
results.
