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..
Direction-Optimizing Breadth-First Search with External Memory Storage
Authors: Shuli Hu, Nathan R. Sturtevant
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the effectiveness of DEBFS we experimented with Rubik s Cube, Chinese Checkers, Top Spin, and the Pancake puzzle. We ran our experiments on a 16-processor 2.4GHz Intel Xeon E5 server with 128GB of RAM, two 8TB disk drives configured as a RAID drive, and one 1.5 TB SSD. |
| Researcher Affiliation | Academia | 1Northeast Normal University, Changchun, Jilin, China 2University of Alberta, Edmonton, AB, Canada |
| Pseudocode | Yes | Pseudo-code for a parallel version of DEBFS is shown in the Algorithm 1. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper conducts experiments on well-known problem domains (Rubik's Cube, Chinese Checkers, etc.) for which Pattern Databases are built, but it does not provide concrete access information (links, citations with authors/year) for publicly available datasets used as input to build these PDBs, nor does it state that the PDBs themselves are publicly released. |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, or test dataset splits; it focuses on building Pattern Databases for known problem domains. |
| Hardware Specification | Yes | We ran our experiments on a 16-processor 2.4GHz Intel Xeon E5 server with 128GB of RAM, two 8TB disk drives configured as a RAID drive, and one 1.5 TB SSD. |
| Software Dependencies | No | The paper mentions software components and parallelization, but it does not provide specific version numbers for any libraries, frameworks, or compilers used in the experiments. |
| Experiment Setup | Yes | We vary the memory and number of buckets used in each experiment, to simulate performance on machines with less RAM. The experiments in this paper use four bits per state... Table 1 provides details such as 'Buckets' and 'RAM used' for various configurations. |