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..
Learning Verified Safe Neural Network Controllers for Multi-Agent Path Finding
Authors: Mingyue Zhang, Nianyu Li, Yi Chen, Jialong Li, Xiao-Yi Zhang, Hengjun Zhao, Jiamou Liu, Wu Chen
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach through shape formation experiments and UAV simulations, demonstrating significant improvements in safety and effectiveness in complex multi-agent environments. Experimental results are indeed promising. |
| Researcher Affiliation | Academia | 1College of Computer and Information Science, Southwest University, Chongqing, China 2 Zhongguancun Laboratory (ZGC Lab), Beijing, China 3Department of Computer Science and Engineering, Waseda University, Tokyo, Japan 4School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing, China 5School of Computer Science, University of Auckland, Auckland, New Zealand |
| Pseudocode | No | The paper describes the methodology and framework in prose and uses figures (e.g., Figure 1: Our framework, Figure 2: The loss function and neural network architecture) but does not contain explicit pseudocode blocks or algorithm sections. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to code repositories for the described methodology. |
| Open Datasets | Yes | As shown in Fig.4, we have chosen three types of scenarios for simulation evaluation: (1) a random map, characterized by cuboidal obstacles that are positioned arbitrarily; (2) a cross map, wherein the spatial arrangement of obstacles emulates a crossroads; (3) a street map, abstracted from the open building dataset pertaining to Portland, USA (Burian et al. 2002). |
| Dataset Splits | No | The paper mentions training and testing phases and variations in the number of drones and maps used for testing (e.g., 'use a random map during training. In the testing phase, the number of drones is expanded from 4 to 1024, and the maps in-'), but it does not specify exact percentages, sample counts, or refer to standard splits for the dataset used. |
| Hardware Specification | Yes | All the simulation experiments are conducted on a desktop running Ubuntu 16, powered by Intel(R) Core(TM) i7-7700 CPU@3.6GHz and an Nvidia Quadro P600 GPU. |
| Software Dependencies | No | The paper mentions 'Ubuntu 16' as the operating system and 'Marabou' as a tool ('We verify hθ i stu,i using the Marabou (Katz et al. 2019)'), but it does not specify version numbers for Marabou or any other critical software libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | c1, c2, c3, c4, c5 are set to 1, 1, 1, 0.1, and 0.05, respectively. γ1 = γ2 = γ3 = 0.01. |