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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |