# Representation Learning for Classical Planning from Partially Observed Traces

arXiv: https://arxiv.org/abs/1907.08352 · HTML: https://mlsanity.com/paper/1907.08352/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems](https://mlsanity.com/paper/2508.13371/) (2025)

## Abstract (arXiv)

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.