Representation Learning for Classical Planning from Partially Observed
  Traces
  Specifying a complete domain model is time-consuming, which has been a
bottleneck of AI planning technique application in many real-world scenarios.
Most classical domain-model learning approaches output a domain model in the
form of the declarative planning language, such as STRIPS or PDDL, and solve
new planning instances by invoking an existing planner. However, planning in
such a representation is sensitive to the accuracy of the learned domain model
which probably cannot be used to solve real planning problems. In this paper,
to represent domain models in a vectorization representation way, we propose a
novel framework based on graph neural network (GNN) integrating model-free
learning and model-based planning, called LP-GNN. By embedding propositions and
actions in a graph, the latent relationship between them is explored to form a
domain-specific heuristics. We evaluate our approach on five classical planning
domains, comparing with the classical domain-model learner ARMS. The
experimental results show that the domain models learned by our approach are
much more effective on solving real planning problems.
