Optimal Pathfinding on Weighted Grid Maps
Authors: Mark Carlson, Sajjad K. Moghadam, Daniel D. Harabor, Peter J. Stuckey, Morteza Ebrahimi
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct a wide-ranging experimental evaluation, including data from real games. Results indicate JPSW is up to orders of magnitude faster than the nearest baseline, online search using A*. |
| Researcher Affiliation | Academia | 1Department of Data Science and Artificial Intelligence, Monash University 2University of Tehran |
| Pseudocode | Yes | We give a complete theoretical description of the new algorithm, including pseudo-code. Algorithm 1 Prospective g update. Algorithm 2 Caching Jump Procedure. |
| Open Source Code | Yes | Full code is available at bitbucket.org/dharabor/pathfinding. |
| Open Datasets | Yes | Moving AI (Sturtevant 2012). The Island set from (Sturtevant et al. 2019). The Frozen Sea from Star Craft, available at Moving AI. |
| Dataset Splits | No | The paper describes the datasets used and the number of queries run but does not specify explicit train/validation/test splits with percentages or counts for model training or validation. |
| Hardware Specification | Yes | The experiments were run on an AMD Ryzen 9 5950X clocked at 4.6GHz with 16GB 3200MHz DDR4 memory. |
| Software Dependencies | No | we implemented JPSW in C++ using the warthog pathfinding research library. No specific version numbers for C++ or the warthog library are provided. |
| Experiment Setup | Yes | We generated 1000 random queries for each map in this set. Each experiment computes the average total time to answer a set of queries on each map in the benchmark set over five runs. The terrain is arranged in stripes of various combinations of widths (64, 128, and 256) and at various angles (0 , 5 , 24 , and 45 ). |