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..
Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions
Authors: Jingwei Chen, Robert C. Holte, Sandra Zilles, Nathan R. Sturtevant
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that NBS competes with or outperforms existing bidirectional search algorithms, and often outperforms A* as well. |
| Researcher Affiliation | Academia | Jingwei Chen University of Denver Denver, CO, USA EMAIL; Robert C. Holte University of Alberta Edmonton, AB, Canada EMAIL; Sandra Zilles University of Regina Regina, SK, Canada EMAIL; Nathan R. Sturtevant University of Denver Denver, CO, USA EMAIL |
| Pseudocode | Yes | The pseudocode for NBS is shown in Algorithms 1 and 2. [...] Algorithm 3 NBS pseudocode for selecting the best pair from Open list. |
| Open Source Code | No | The paper does not provide any statement or link indicating the public availability of the source code for the described methodology. |
| Open Datasets | Yes | In Table 1 we present results on problems from four different domains, including grid-based pathfinding problems [Sturtevant, 2012] ( brc maps from Dragon Age: Origins (DAO)), random 4-peg Tower of Hanoi (TOH) problems, random pancake puzzles, and the standard 15 puzzle instances [Korf, 1985]. |
| Dataset Splits | No | The paper mentions using standard benchmark problems but does not explicitly provide specific details on training, validation, and test dataset splits (e.g., percentages, counts, or explicit standard split names). |
| Hardware Specification | No | The paper does not provide specific details on the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks) used in the experiments. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings used in the experiments. |