Safe Learning of Lifted Action Models
  Creating a domain model, even for classical, domain-independent planning, is
a notoriously hard knowledge-engineering task. A natural approach to solve this
problem is to learn a domain model from observations. However, model learning
approaches frequently do not provide safety guarantees: the learned model may
assume actions are applicable when they are not, and may incorrectly capture
actions' effects. This may result in generating plans that will fail when
executed. In some domains such failures are not acceptable, due to the cost of
failure or inability to replan online after failure. In such settings, all
learning must be done offline, based on some observations collected, e.g., by
some other agents or a human. Through this learning, the task is to generate a
plan that is guaranteed to be successful. This is called the model-free
planning problem. Prior work proposed an algorithm for solving the model-free
planning problem in classical planning. However, they were limited to learning
grounded domains, and thus they could not scale. We generalize this prior work
and propose the first safe model-free planning algorithm for lifted domains. We
prove the correctness of our approach, and provide a statistical analysis
showing that the number of trajectories needed to solve future problems with
high probability is linear in the potential size of the domain model. We also
present experiments on twelve IPC domains showing that our approach is able to
learn the real action model in all cases with at most two trajectories.
