The FastMap Algorithm for Shortest Path Computations
Authors: Liron Cohen, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven Koenig, T. K. Satish Kumar
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate that A* search using the Fast Map heuristic is competitive with A* search using other state-of-the-art heuristics, such as the Differential heuristic. We performed experiments on many benchmark maps from [Sturtevant, 2012]. Figure 3 presents representative results. |
| Researcher Affiliation | Academia | 1University of Southern California 2University of California, Irvine |
| Pseudocode | Yes | ALGORITHM 1: Shows the Fast Map algorithm. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We performed experiments on many benchmark maps from [Sturtevant, 2012]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or test sets. It mentions '1000 random instances' for experiments but no explicit splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings for the experiments. |