Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning
Authors: Silvan Sievers, Florian Pommerening, Thomas Keller, Malte Helmert
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that SCP significantly improves M&S on standard planning benchmarks. We implemented our techniques on top of the M&S implementation of Fast Downward [Helmert, 2006], version 19.12. To evaluate them, we use the following state-of-the-art M&S configuration... Experiments were run on Intel Xeon Silver 4114 CPUs... |
| Researcher Affiliation | Academia | University of Basel, Switzerland {silvan.sievers,florian.pommerening,tho.keller,malte.helmert}@unibas.ch |
| Pseudocode | Yes | Algorithm 1 Merge-and-shrink algorithm extended to compute SCP heuristics. |
| Open Source Code | Yes | The code, benchmarks, and experimental data are published online [Sievers et al., 2020a]. Sievers et al., 2020a] Silvan Sievers, Florian Pommerening, Thomas Keller, and Malte Helmert. Code, benchmarks and experiment data for the IJCAI 2020 paper Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning . https://doi.org/10.5281/zenodo. 3775871, 2020. |
| Open Datasets | Yes | As benchmarks, we use the tasks of all optimal tracks of all International Planning Competitions, a set consisting of 1827 tasks across 65 domains. The code, benchmarks, and experimental data are published online [Sievers et al., 2020a]. |
| Dataset Splits | No | The paper uses a set of planning benchmark tasks but does not describe any specific training, validation, or test dataset splits as is common in machine learning models. |
| Hardware Specification | Yes | Experiments were run on Intel Xeon Silver 4114 CPUs |
| Software Dependencies | Yes | We implemented our techniques on top of the M&S implementation of Fast Downward [Helmert, 2006], version 19.12. |
| Experiment Setup | Yes | To evaluate them, we use the following state-of-the-art M&S configuration: shrinking is based on bisimulation and uses a size limit of 50000 on transition systems (allowing exact shrinking also when shrinking is not necessary to respect the size limit); the merge strategy is SCC-DFP [Sievers et al., 2016]; we use exact label reduction; and the main loop is limited to 900s. Each planner run is limited to 1800s and 3.5 Gi B. |