  The assumption of complete domain knowledge is not warranted for robot
planning and decision-making in the real world. It could be due to design flaws
or arise from domain ramifications or qualifications. In such cases, existing
planning and learning algorithms could produce highly undesirable behaviors.
This problem is more challenging than partial observability in the sense that
the agent is unaware of certain knowledge, in contrast to it being partially
observable: the difference between known unknowns and unknown unknowns. In this
work, we formulate it as the problem of Domain Concretization, an inverse
problem to domain abstraction. Based on an incomplete domain model provided by
the designer and teacher traces from human users, our algorithm searches for a
candidate model set under a minimalistic model assumption. It then generates a
robust plan with the maximum probability of success under the set of candidate
models. In addition to a standard search formulation in the model-space, we
propose a sample-based search method and also an online version of it to
improve search time. We tested our approach on IPC domains and a simulated
robotics domain where incompleteness was introduced by removing domain features
from the complete model. Results show that our planning algorithm increases the
plan success rate without impacting the cost much.
