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Specifying a complete domain model is time-consuming, which has been abottleneck of AI planning technique application in many real-world scenarios.Most classical domain-model learning approaches output a domain model in theform of the declarative planning language, such as STRIPS or PDDL, and solvenew planning instances by invoking an existing planner. However, planning insuch a representation is sensitive to the accuracy of the learned domain modelwhich probably cannot be used to solve real planning problems. In this paper,to represent domain models in a vectorization representation way, we propose anovel framework based on graph neural network (GNN) integrating model-freelearning and model-based planning, called LP-GNN. By embedding propositions andactions in a graph, the latent relationship between them is explored to form adomain-specific heuristics. We evaluate our approach on five classical planningdomains, comparing with the classical domain-model learner ARMS. Theexperimental results show that the domain models learned by our approach aremuch more effective on solving real planning problems.

Representation Learning for Classical Planning from Partially Observed Traces