  Learning of user preferences, as represented by, for example, Conditional
Preference Networks (CP-nets), has become a core issue in AI research. Recent
studies investigate learning of CP-nets from randomly chosen examples or from
membership and equivalence queries. To assess the optimality of learning
algorithms as well as to better understand the combinatorial structure of
classes of CP-nets, it is helpful to calculate certain learning-theoretic
information complexity parameters. This article focuses on the frequently
studied case of learning from so-called swap examples, which express
preferences among objects that differ in only one attribute. It presents bounds
on or exact values of some well-studied information complexity parameters,
namely the VC dimension, the teaching dimension, and the recursive teaching
dimension, for classes of acyclic CP-nets. We further provide algorithms that
learn tree-structured and general acyclic CP-nets from membership queries.
Using our results on complexity parameters, we assess the optimality of our
algorithms as well as that of another query learning algorithm for acyclic
CP-nets presented in the literature. Our algorithms are near-optimal, and can,
under certain assumptions, be adapted to the case when the membership oracle is
faulty.
