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..
Implicit Coordination in Crowded Multi-Agent Navigation
Authors: Julio Godoy, Ioannis Karamouzas, Stephen Guy, Maria Gini
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our coordination approach in a variety of scenarios and show that its performance scales to scenarios with hundreds of agents. |
| Researcher Affiliation | Academia | Julio Godoy and Ioannis Karamouzas and Stephen J. Guy and Maria Gini Department of Computer Science and Engineering University of Minnesota 200 Union St SE, Minneapolis MN 55455 |
| Pseudocode | Yes | Algorithm 1: The C-Nav framework for agent i; Algorithm 2: Compute most similar neighbors of i; Algorithm 3: Compute most constrained neighbors of i; Algorithm 4: Sim Motion(a) for agent i |
| Open Source Code | No | The paper includes a link for videos related to the results ("http://motion.cs.umn.edu/r/CNAV/ for videos") but does not explicitly state that the source code for the methodology is provided. |
| Open Datasets | No | The paper describes scenarios (Circle, Bidirectional, Crowd) but does not provide specific access information (link, DOI, formal citation with authors/year) for publicly available datasets. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments were provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., ORCA version, Python version, library versions) were mentioned in the paper. |
| Experiment Setup | Yes | In all our experiments, we used ORCA s default settings for the agent s radii (0.5 m), sensing range (15 m) and maximum number of agents sensed (num Neighs=10). We set T=2 timesteps, υmax=1.5 m/s, γ=0.9, k=3 and s=3. The timestep duration, Δt, is set to 25 ms. |