Graph Learning for Numeric Planning
Authors: Dillon Chen, Sylvie Thiebaux
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our graph kernels are vastly more efficient and generalise better than graph neural networks for numeric planning, and also yield competitive coverage performance compared to domain-independent numeric planners. |
| Researcher Affiliation | Academia | Dillon Z. Chen1,2 Sylvie Thiébaux1,2 1LAAS-CNRS, University of Toulouse 2The Australian National University {dillon.chen,sylvie.thiebaux}@laas.fr |
| Pseudocode | Yes | Algorithm 1: CCWL algorithm |
| Open Source Code | Yes | Code is available at https://github.com/Dillon ZChen/goose |
| Open Datasets | Yes | We take 8 domains out of 10 domains from the International Planning Competition 2023 Learning Track (IPC-LT) [SSA23] and either convert them to equivalent numeric formulations, or introduce numeric variables to model extra features such as capacity constraints. |
| Dataset Splits | No | The paper mentions '90 testing problems and at most 99 small training problems' but does not explicitly detail a validation split or set. |
| Hardware Specification | Yes | All baselines and models are run on a single Intel Xeon Platinum 8268 (2.90 GHz) core with a 5 minute timeout for search and 8GB of main memory. |
| Software Dependencies | No | The paper mentions 'CPLEX version 22.11' but does not list multiple key software components with their specific version numbers (e.g., programming languages, other libraries or frameworks). |
| Experiment Setup | Yes | Each GNN has a hidden dimension of 64, and is trained with the Adam optimiser [KB15] with an initial learning rate of 10 3 and batch size of 16. A scheduler reduces the training loss by a factor of 10 if loss does not improve after 10 epochs. Training then terminates if the learning rate falls below 10 5. |