Learning Probabilistic Hierarchical Task Networks to Capture User
  Preferences
  We propose automatically learning probabilistic Hierarchical Task Networks
(pHTNs) in order to capture a user's preferences on plans, by observing only
the user's behavior. HTNs are a common choice of representation for a variety
of purposes in planning, including work on learning in planning. Our
contributions are (a) learning structure and (b) representing preferences. In
contrast, prior work employing HTNs considers learning method preconditions
(instead of structure) and representing domain physics or search control
knowledge (rather than preferences). Initially we will assume that the observed
distribution of plans is an accurate representation of user preference, and
then generalize to the situation where feasibility constraints frequently
prevent the execution of preferred plans. In order to learn a distribution on
plans we adapt an Expectation-Maximization (EM) technique from the discipline
of (probabilistic) grammar induction, taking the perspective of task reductions
as productions in a context-free grammar over primitive actions. To account for
the difference between the distributions of possible and preferred plans we
subsequently modify this core EM technique, in short, by rescaling its input.
