Learning Probabilistic CP-nets from Observations of Optimal Items

Modelling preferences has been an active research topic in Artificial Intelligence for more than fifteen years. Existing formalisms are rich and flexible enough to capture the behaviour of complex decision rules. However, for being interesting in practice, it is interesting to learn not a single model, but a probabilistic model that can compactly represent the preferences of a group of users - this model can then be finely tuned to fit one particular user. Even in contexts where a user is not anonymous, her preferences can depend on the value of a non controllable state variable. In such contexts, we would like to be able to answer questions like "What is the probability that o is preferred to o' by some (unknown) agent?", or "Which item is most likely to be the preferred one, given some constraints?" We study in this paper how Probabilistic Conditional Preference networks can be learnt, both in off-line and on-line settings.