Synthesizing Robust Plans under Incomplete Domain Models
  Most current planners assume complete domain models and focus on generating
correct plans. Unfortunately, domain modeling is a laborious and error-prone
task. While domain experts cannot guarantee completeness, often they are able
to circumscribe the incompleteness of the model by providing annotations as to
which parts of the domain model may be incomplete. In such cases, the goal
should be to generate plans that are robust with respect to any known
incompleteness of the domain. In this paper, we first introduce annotations
expressing the knowledge of the domain incompleteness, and formalize the notion
of plan robustness with respect to an incomplete domain model. We then propose
an approach to compiling the problem of finding robust plans to the conformant
probabilistic planning problem. We present experimental results with
Probabilistic-FF, a state-of-the-art planner, showing the promise of our
approach.
