Probabilistic Conditional Preference Networks
  In order to represent the preferences of a group of individuals, we introduce
Probabilistic CP-nets (PCP-nets). PCP-nets provide a compact language for
representing probability distributions over preference orderings. We argue that
they are useful for aggregating preferences or modelling noisy preferences.
Then we give efficient algorithms for the main reasoning problems, namely for
computing the probability that a given outcome is preferred to another one, and
the probability that a given outcome is optimal. As a by-product, we obtain an
unexpected linear-time algorithm for checking dominance in a standard,
tree-structured CP-net.
