# Learning Domain-Independent Heuristics for Grounded and Lifted Planning

arXiv: https://arxiv.org/abs/2312.11143 · HTML: https://mlsanity.com/paper/2312.11143/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Graph Neural Networks with Learnable Structural and Positional Representations](https://mlsanity.com/paper/2110.07875/) (2021)
- [The Surprising Power of Graph Neural Networks with Random Node Initialization](https://mlsanity.com/paper/2010.01179/) (2020)
- [Neural Logic Machines](https://mlsanity.com/paper/1904.11694/) (2019)
- [Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks](https://mlsanity.com/paper/2203.00199/) (2022)
- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Learning Domain-Independent Planning Heuristics with Hypergraph Networks](https://mlsanity.com/paper/1911.13101/) (2019)
- [ASNets: Deep Learning for Generalised Planning](https://mlsanity.com/paper/1908.01362/) (2019)
- [Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits](https://mlsanity.com/paper/2109.10129/) (2021)
- [Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning](https://mlsanity.com/paper/2007.06702/) (2020)
- [Learning Generalized Policies Without Supervision Using GNNs](https://mlsanity.com/paper/2205.06002/) (2022)
- [Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators](https://mlsanity.com/paper/2109.14830/) (2021)
- [Online Planner Selection with Graph Neural Networks and Adaptive Scheduling](https://mlsanity.com/paper/1811.00210/) (2018)

## Built on by (most cited first, 30 of 31)

- [Can Graph Learning Improve Planning in LLM-based Agents?](https://mlsanity.com/paper/2405.19119/) (2024)
- [Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning](https://mlsanity.com/paper/2403.16508/) (2024)
- [Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code](https://mlsanity.com/paper/2503.18809/) (2025)
- [Successor-Generator Planning with LLM-generated Heuristics](https://mlsanity.com/paper/2501.18784/) (2025)
- [Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies](https://mlsanity.com/paper/2508.18507/) (2025)
- [Graph Learning for Numeric Planning](https://mlsanity.com/paper/2410.24080/) (2024)
- [HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning](https://mlsanity.com/paper/2406.00965/) (2024)
- [Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems](https://mlsanity.com/paper/2406.02598/) (2024)
- [Leveraging Action Relational Structures for Integrated Learning and Planning](https://mlsanity.com/paper/2504.20318/) (2025)
- [CoS: Towards Optimal Event Scheduling via Chain-of-Scheduling](https://mlsanity.com/paper/2511.12913/) (2025)
- [PDDLFuse: A Tool for Generating Diverse Planning Domains](https://mlsanity.com/paper/2411.19886/) (2024)
- [Graph Neural Network Based Action Ranking for Planning](https://mlsanity.com/paper/2412.04752/) (2024)
- [Deep Learning for Generalised Planning with Background Knowledge](https://mlsanity.com/paper/2410.07923/) (2024)
- [Counting and Reasoning with Plans](https://mlsanity.com/paper/2502.00145/) (2025)
- [Learning Efficiency Meets Symmetry Breaking](https://mlsanity.com/paper/2504.19738/) (2025)
- [Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study](https://mlsanity.com/paper/2508.18515/) (2025)
- [Learning Admissible Heuristics for A*: Theory and Practice](https://mlsanity.com/paper/2509.22626/) (2025)
- [Choosing a Classical Planner with Graph Neural Networks](https://mlsanity.com/paper/2402.04874/) (2024)
- [WLPlan: Relational Features for Symbolic Planning](https://mlsanity.com/paper/2411.00577/) (2024)
- [LLM-Evolved Domain-Independent Heuristics for Symbolic AI Planning](https://mlsanity.com/paper/2605.29649/) (2026)
- [Graph Learning for Planning: The Story Thus Far and Open Challenges](https://mlsanity.com/paper/2412.02136/) (2024)
- [Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning](https://mlsanity.com/paper/2505.13372/) (2025)
- [Relational GNNs Cannot Learn C2 Features for Planning](https://mlsanity.com/paper/2506.11721/) (2025)
- [Symmetry-Invariant Novelty Heuristics via Unsupervised Weisfeiler-Leman Features](https://mlsanity.com/paper/2508.18520/) (2025)
- [From domain-landmark graph learning to problem-landmark graph generation](https://mlsanity.com/paper/2509.17062/) (2025)
- [Two Constraint Compilation Methods for Lifted Planning](https://mlsanity.com/paper/2511.10164/) (2025)
- [Per-Domain Generalizing Policies: On Learning Efficient and Robust Q-Value Functions (Extended Version with Technical Appendix)](https://mlsanity.com/paper/2603.17544/) (2026)
- [Property-Guided LLM Program Synthesis for Planning](https://mlsanity.com/paper/2605.16142/) (2026)
- [Learning to Search and Searching to Learn for Generalization in Planning](https://mlsanity.com/paper/2605.25720/) (2026)
- [LLM-Evolved Pattern Generators for Optimal Classical Planning](https://mlsanity.com/paper/2606.02438/) (2026)

## Abstract (arXiv)

We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular, to mitigate the issues caused by large grounded GNNs we present the first method for learning domain-independent heuristics with only the lifted representation of a planning task. We also provide a theoretical analysis of the expressiveness of our models, showing that some are more powerful than STRIPS-HGN, the only other existing model for learning domain-independent heuristics. Our experiments show that our heuristics generalise to much larger problems than those in the training set, vastly surpassing STRIPS-HGN heuristics.