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 with an Adaptive Delay-Based Heuristic
Authors: Thomy Phan, Benran Zhang, Shao-Hung Chan, Sven Koenig
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate ADDRESS in multiple maps from the MAPF benchmark set and demonstrate cost improvements by at least 50% in large-scale scenarios with up to a thousand agents, compared with the original MAPF-LNS and other state-of-the-art methods. |
| Researcher Affiliation | Academia | 1University of Southern California 2University of California, Irvine EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: ADDRESS Destroy Heuristic Algorithm 2: MAPF-LNS with our ADDRESS Heuristic |
| Open Source Code | Yes | Code https://github.com/Jimy Z13/ADDRESS |
| Open Datasets | Yes | We evaluate ADDRESS on five maps from the MAPF benchmark set of (Stern et al. 2019), namely (1) a Random map (Random-32-32-20), (2) two Game maps Ost003d and (3) Den520d, (4) a Warehouse map (Warehouse-20-40-10-2-2), and (5) a City map (Paris 1 256). All maps have different sizes and structures. We conduct all experiments on the publicly available 25 random scenarios per map. |
| Dataset Splits | No | We conduct all experiments on the publicly available 25 random scenarios per map. This text describes the instances used for evaluation but does not specify a training/test/validation split of a dataset for model training or evaluation. The paper focuses on an algorithmic solution rather than a machine learning model that would typically require such splits. |
| Hardware Specification | Yes | All experiments were run on a high-performance computing cluster with Cent OS Linux, Intel Xeon 2640v4 CPUs, and 64 GB RAM. |
| Software Dependencies | No | Our implementation is based on the public code of (Li et al. 2022; Phan et al. 2024b). This indicates reliance on other codebases but does not specify versions of programming languages, libraries, or solvers used in the current implementation. |
| Experiment Setup | Yes | We set the neighborhood size N = 8 (except for BALANCE, which automatically adapts N), K = 32, and use Thompson Sampling for ADDRESS and BALANCE, unless stated otherwise. ϵ-Greedy is used with ϵ = 1/2. |