  Planning with preferences has been employed extensively to quickly generate
high-quality plans. However, it may be difficult for the human expert to supply
this information without knowledge of the reasoning employed by the planner and
the distribution of planning problems. We consider the problem of actively
eliciting preferences from a human expert during the planning process.
Specifically, we study this problem in the context of the Hierarchical Task
Network (HTN) planning framework as it allows easy interaction with the human.
Our experimental results on several diverse planning domains show that the
preferences gathered using the proposed approach improve the quality and speed
of the planner, while reducing the burden on the human expert.
