Periodic Multi-Agent Path Planning
Authors: Kazumi Kasaura, Ryo Yonetani, Mai Nishimura
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the effectiveness of our method for first solving periodic MAPP then using periodic plans for online MAPP problems, we focus on scenarios of abstracting AIM tasks (Dresner and Stone 2008). |
| Researcher Affiliation | Industry | OMRON SINIC X Corporation Hongo 5-24-5, Bunkyo-ku, Tokyo, Japan |
| Pseudocode | No | The paper refers to concrete algorithms and implementation details in Appendix B and C, but it does not include any clearly labeled pseudocode or algorithm blocks in the main text. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology, nor does it include any links to a code repository. |
| Open Datasets | No | The paper describes generating '10 different problem instances' for 'each environment and each λ' but these are custom scenarios (e.g., 'AIM scenario involves a single intersection with several entrances and exits') and no concrete access information (link, DOI, repository, or formal citation to a public dataset) is provided. |
| Dataset Splits | No | The paper describes generating '10 different problem instances' for evaluation but does not specify explicit train/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9', or 'CPLEX 12.4') that would be necessary to replicate the experiments. |
| Experiment Setup | Yes | The paper provides specific parameters for the experimental setup, such as agent radius 'r fixed to 0.5' and maximum velocity 'vmax fixed to 1.0'. It also details the modeling of agent appearance times: 'The time interval between agent appearances is modeled as 1.0 + α, where α follows the exponential distribution with a rate parameter λ.' |