# Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

arXiv: https://arxiv.org/abs/1811.00210 · HTML: https://mlsanity.com/paper/1811.00210/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 13 of 13)

- [Directed Acyclic Graph Neural Networks](https://mlsanity.com/paper/2101.07965/) (2021)
- [Learning Domain-Independent Heuristics for Grounded and Lifted Planning](https://mlsanity.com/paper/2312.11143/) (2023)
- [Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators](https://mlsanity.com/paper/2109.14830/) (2021)
- [Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning](https://mlsanity.com/paper/2404.05235/) (2024)
- [Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation](https://mlsanity.com/paper/2301.13279/) (2023)
- [Graph Learning for Numeric Planning](https://mlsanity.com/paper/2410.24080/) (2024)
- [Meta-Reinforcement Learning for Heuristic Planning](https://mlsanity.com/paper/2107.02603/) (2021)
- [Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks](https://mlsanity.com/paper/2203.15030/) (2022)
- [Choosing a Classical Planner with Graph Neural Networks](https://mlsanity.com/paper/2402.04874/) (2024)
- [TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs](https://mlsanity.com/paper/2506.17894/) (2025)
- [Graph Learning for Planning: The Story Thus Far and Open Challenges](https://mlsanity.com/paper/2412.02136/) (2024)
- [Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations](https://mlsanity.com/paper/2507.04356/) (2025)
- [RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation](https://mlsanity.com/paper/2608.05646/) (2026)

## Abstract (arXiv)

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at urlhttps://github.com/matenure/GNNplanner.