Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Cost-Partitioned Merge-and-Shrink Heuristics for Optimal Classical Planning
Authors: Silvan Sievers, Florian Pommerening, Thomas Keller, Malte Helmert
IJCAI 2020 | Venue PDF | 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 EMAIL |
| 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. |