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..
Factored Symmetries for Merge-and-Shrink Abstractions
Authors: Silvan Sievers, Martin Wehrle, Malte Helmert, Alexander Shleyfman, Michael Katz
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also devise practical merging strategies based on this concept and experimentally validate their utility. |
| Researcher Affiliation | Collaboration | Silvan Sievers and Martin Wehrle and Malte Helmert University of Basel, Switzerland EMAIL Alexander Shleyfman Technion, Haifa, Israel EMAIL Michael Katz IBM Haifa Research Lab, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1 Symmetry-based merge-and-shrink. |
| Open Source Code | No | The paper mentions using third-party tools like 'Fast Downward planning system' and 'Bliss', but does not state that the code implementing their proposed factored symmetries or merging strategies is open-source or available. |
| Open Datasets | No | The paper mentions using "all optimal IPC benchmarks up to 2011", but it does not provide a specific link, DOI, or formal citation with author/year information to access these benchmarks directly. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | Yes | Our experiments are performed on machines with Intel Xeon E5-2660 CPUs running at 2.2 GHz, using a time bound of 30 minutes and a memory bound of 2 GB per run. |
| Software Dependencies | No | The paper mentions 'Fast Downward planner (Helmert 2006)' and 'Bliss (Junttila and Kaski 2007)' but does not provide specific version numbers for these software dependencies, which are necessary for reproduction. |
| Experiment Setup | Yes | Our experiments are performed on machines with Intel Xeon E5-2660 CPUs running at 2.2 GHz, using a time bound of 30 minutes and a memory bound of 2 GB per run. ... We limit the overall time budget for Bliss to T = 60 seconds... We focus on the shrinking strategy based on bisimulation B with limit N = 50000 for the maximal size of all transition systems during the merge-and-shrink computation. |