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..
Scalable Mechanism Design for Multi-Agent Path Finding
Authors: Paul Friedrich, Yulun Zhang, Michael Curry, Ludwig Dierks, Stephen McAleer, Jiaoyang Li, Tuomas Sandholm, Sven Seuken
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments verify that our mechanisms behavior follows our claims on optimality, scalability, and payments. Our benchmarks are strategyproof mechanisms whose allocation rule solves classical MAPF, i Bundle [Amir et al., 2015] being the most advanced competition. Figure 1 compares the success rate, runtime and welfare over FCFS of MCPP with different sample sizes m on the random-32-32-20 map. |
| Researcher Affiliation | Collaboration | 1ETH AI Center 2University of Zurich 3Carnegie Mellon University 4Harvard University 5University of Illinois at Chicago 6Strategy Robot, Inc., Strategic Machine, Inc., Optimized Markets, Inc. |
| Pseudocode | Yes | Mechanism 1: MCPP Input: Agent reports ( 1, . . . , n), No. of samples m Output: Con๏ฌict-free paths ( 1, . . . , n) and payments (p1, . . . , pn) Allocation rule: Prioritized Planning |
| Open Source Code | Yes | Code at https://github.com/lunjohnzhang/MAPF-Mechanism. |
| Open Datasets | Yes | We evaluate payment-CBS (PCBS), exhaustive-PBS (EPBS) and Monte-Carlo prioritized planning (MCPP) on four 2D maps selected from the commonly used MAPF benchmarks by Stern et al. [2019]. |
| Dataset Splits | No | The paper evaluates mechanisms on instances but does not provide specific train/validation/test dataset splits (percentages or counts), nor does it describe cross-validation setups or predefined split files. It mentions '100 instances' for each run but not how those instances are split for training, validation, or testing. |
| Hardware Specification | Yes | Our code was implemented in C++ and ran on a 2x12-core Intel Xeon E5-2650v4 machine with 128 GB of RAM. |
| Software Dependencies | No | Our code was implemented in C++ and ran on a 2x12-core Intel Xeon E5-2650v4 machine with 128 GB of RAM. The paper only mentions 'C++' as the implementation language but does not provide specific software dependencies with version numbers (e.g., specific compilers, libraries, or frameworks with their versions). |
| Experiment Setup | Yes | MCPP uses a sample size of m = 100, except in Figure 1. Each run uses a set of 100 instances. We set a runtime limit for all mechanisms at 3600 seconds for the random-32-32-20 and dens312d maps and 5400 seconds for the dens520d and Paris-1-256 maps. |