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..
Conditions for Avoiding Node Re-expansions in Bounded Suboptimal Search
Authors: Jingwei Chen, Nathan R. Sturtevant
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results compare priority functions within this class to see if the new priority functions outperform WA*. Several standard domains are used for testing. |
| Researcher Affiliation | Academia | Jingwei Chen and Nathan R. Sturtevant Department of Computing Science, University of Alberta, Edmonton, AB Canada EMAIL |
| Pseudocode | Yes | Algorithm 1 Generic Best-First Search |
| Open Source Code | No | The paper references prior work and experimental results, but does not include any explicit statement about providing open-source code for the methodology described in this paper, nor does it provide a link to such code. |
| Open Datasets | Yes | On grid maps we tested on 15,928 problem instances from the Dragon Age: Origins (DAO) map set, 6,444 problem instances from the Star Craft 1 (SC1) map set, and on 35,360 problem instances on maps with 40% random obstacles [Sturtevant, 2012]. The instance are the standard 100 Korf instance [Korf, 1985]. Finally, we followed the heavy variant of pancake puzzle created by Gilon et al. [2016], where the cost of flipping a prefix (V [1] V [i + 1]) is the max(V [1]; V [i + 1]). |
| Dataset Splits | No | The paper describes the datasets used and the number of instances, but it does not specify any training, validation, or test splits, nor does it mention cross-validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions different algorithms and heuristics used (e.g., WA*, A*epsilon, MD, HGAP heuristic) but does not provide specific software names with version numbers that would be necessary to replicate the experiment environment. |
| Experiment Setup | Yes | The experimental results compare priority functions within this class to see if the new priority functions outperform WA*. Several standard domains are used for testing. We experiment with the various weights for WA*, and best-first search with the convex downward parabola (ΦXDP ) and the convex upward parabola (ΦXUP ). |