Planning with Partial Preference Models
  Current work in planning with preferences assume that the user's preference
models are completely specified and aim to search for a single solution plan.
In many real-world planning scenarios, however, the user probably cannot
provide any information about her desired plans, or in some cases can only
express partial preferences. In such situations, the planner has to present not
only one but a set of plans to the user, with the hope that some of them are
similar to the plan she prefers. We first propose the usage of different
measures to capture quality of plan sets that are suitable for such scenarios:
domain-independent distance measures defined based on plan elements (actions,
states, causal links) if no knowledge of the user's preferences is given, and
the Integrated Convex Preference measure in case the user's partial preference
is provided. We then investigate various heuristic approaches to find set of
plans according to these measures, and present empirical results demonstrating
the promise of our approach.
