Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Anytime Multi-Agent Path Finding via Large Neighborhood Search
Authors: Jiaoyang Li, Zhe Chen, Daniel Harabor, Peter J. Stuckey, Sven Koenig
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithm MAPF-LNS in an extensive set of experiments and report large gains over a variety of competing algorithms from the recent literature |
| Researcher Affiliation | Academia | 1University of Southern California, USA 2Monash University, Australia |
| Pseudocode | Yes | Algorithm 1: Generate an agent-based neighborhood. |
| Open Source Code | Yes | Our implementation is available at https://github.com/Jiaoyang-Li/MAPF-LNS. |
| Open Datasets | Yes | We evaluate our MAPF-LNS on six representative maps from the MAPF benchmark suite [Stern et al., 2019] |
| Dataset Splits | No | The paper evaluates an algorithm for Multi-Agent Path Finding (MAPF) on benchmark instances. It does not involve training a machine learning model, and therefore, explicit training, validation, and test dataset splits in that context are not provided or relevant. |
| Hardware Specification | Yes | the experiments are conducted on Ubuntu 20.04 LTS on an Intel Xeon 8260 CPU with a memory limit of 8 GB and a time limit of 60s |
| Software Dependencies | No | The paper states that 'The algorithms are implemented in C++', but does not provide specific version numbers for any key software libraries, compilers, or dependencies. |
| Experiment Setup | Yes | The algorithms are implemented in C++, and the experiments are conducted on Ubuntu 20.04 LTS on an Intel Xeon 8260 CPU with a memory limit of 8 GB and a time limit of 60s, except for Experiment 1 where the time limit is 10s and Experiment 7 where the time limit is 600s. |