Factored Symmetries for Merge-and-Shrink Abstractions
Authors: Silvan Sievers, Martin Wehrle, Malte Helmert, Alexander Shleyfman, Michael Katz
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 {silvan.sievers,martin.wehrle,malte.helmert}@unibas.ch Alexander Shleyfman Technion, Haifa, Israel alesh@tx.technion.ac.il Michael Katz IBM Haifa Research Lab, Israel katzm@il.ibm.com |
| 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. |