Euclidean Pathfinding with Compressed Path Databases
Authors: Bojie Shen, Muhammad Aamir Cheema, Daniel Harabor, Peter J. Stuckey
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a range of experiments and empirical comparisons we show that: (i) the auxiliary data structures required by the new method are cheap to build and store; (ii) for optimal search, the new algorithm is faster than a range of recent ESPP planners, with speedups ranging from several factors to over one order of magnitude; (iii) for anytime search, where feasible solutions are needed fast, we report even better runtimes. We run experiments on a variety of grid map benchmarks which are described in [Sturtevant, 2012], including 373 game maps from four sets of maps: DAO (156), DA (67), BG (75), SC (75). |
| Researcher Affiliation | Academia | Bojie Shen , Muhammad Aamir Cheema , Daniel D. Harabor and Peter J. Stuckey Faculty of Information Technology, Monash University, Melbourne, Australia {bojie.shen, aamir.cheema, daniel.harabor, peter.stuckey}@monash.edu |
| Pseudocode | Yes | Algorithm 1: End Point Search (EPS) |
| Open Source Code | No | The paper states 'We implemented our algorithm in C++', but it does not provide a specific repository link, explicit code release statement, or indicate code availability in supplementary materials for the methodology described in this paper. It does provide links to code for *comparison* algorithms (e.g., Polyanya, ENLSVG). |
| Open Datasets | Yes | We run experiments on a variety of grid map benchmarks which are described in [Sturtevant, 2012], including 373 game maps from four sets of maps: DAO (156), DA (67), BG (75), SC (75). All benchmarks are available from the HOG2 online repository.1 https://github.com/nathansttt/hog2 |
| Dataset Splits | No | The paper uses pre-existing grid map benchmarks but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | Yes | All the experiments are performed on a 2.6 GHz Intel Core i7 machine with 16GB of RAM and running OSX 10.14.6. |
| Software Dependencies | No | The paper mentions 'We implemented our algorithm in C++' and 'running OSX 10.14.6', but it does not provide specific ancillary software details with version numbers (e.g., specific libraries or solvers used in the C++ implementation). |
| Experiment Setup | No | The paper describes the general computing environment for experiments ('2.6 GHz Intel Core i7 machine with 16GB of RAM and running OSX 10.14.6', 'implemented in C++') but does not provide specific experimental setup details such as hyperparameter values, model initialization, or specific training configurations typical for machine learning models. |