Improved Anonymous Multi-Agent Path Finding Algorithm
Authors: Zain Alabedeen Ali, Konstantin Yakovlev
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, the resultant AMAPF solver demonstrates superior performance compared to the state-of-the-art competitor and is able to solve all publicly available MAPF instances from the wellknown Moving AI benchmark in less than 30 seconds. |
| Researcher Affiliation | Academia | 1 Moscow Institute of Physics and Technology, Moscow, Russia 2 Federal Research Center for Computer Science and Control of the Russian Academy of Sciences, Moscow, Russia 3 AIRI, Moscow, Russia ali.za@phystech.edu, yakovlev@isa.ru |
| Pseudocode | Yes | Algorithm 1 shows the pseudo-code of a graph traversal algorithm with our modifications. ... Algorithm 2: Generating successors |
| Open Source Code | Yes | We have implemented the improved AMAPF solver in C++4 ... 4https://github.com/Path Planning/AMAPF-MF-BS |
| Open Datasets | Yes | The MAPF maps and instances were taken from the publicly available MAPF benchmark (Stern et al. 2019). |
| Dataset Splits | No | The paper describes how instances are run with varying numbers of agents and a time limit, but does not provide specific percentages or counts for training, validation, or test dataset splits. |
| Hardware Specification | Yes | The experiments were conducted on a PC with Intel Core i7-10700F CPU @ 2.90GHz 16 and 32Gb of RAM. |
| Software Dependencies | No | We have implemented the improved AMAPF solver in C++4 ... The paper mentions C++ as the implementation language but does not provide specific software dependencies with version numbers (e.g., specific libraries, compilers, or operating systems used for development or execution). |
| Experiment Setup | No | The paper describes the test methodology including time limits and sequential scenario execution, and how different T values were explored. However, it does not specify hyperparameters or system-level training settings typically found in experimental setups for models. |