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..
Action Selection Methods for Multi-Agent Navigation in Crowded Environments
Authors: Julio Godoy
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results obtained in simulation under different conditions show that the agents reach their destinations faster and use motions that minimize their overall energy consumption. and Figure 2 shows the results (in Interaction Overhead) of comparing two of my proposed approaches, ALAN and C-Nav, with ORCA in three example environments. |
| Researcher Affiliation | Academia | Julio Godoy Department of Computer Science and Engineering University of Minnesota |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks are present in the paper. |
| Open Source Code | No | No explicit statement or link for open-source code release for the described methodology is provided. |
| Open Datasets | No | No specific publicly available dataset is mentioned with a link, DOI, or formal citation (including author names and year in brackets/parentheses). The paper refers to 'simulation under different conditions' and 'three example environments' (Circle, Bidirectional, Crowd), but these are descriptions of scenarios rather than external datasets with access information. |
| Dataset Splits | No | No specific dataset split information (percentages, counts, or predefined splits) is provided for training, validation, or testing. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., library names with versions like Python 3.8, PyTorch 1.9) are provided. |
| Experiment Setup | No | The paper describes the environments used for evaluation (Figure 2a) and mentions a new metric 'Interaction Overhead' for comparison, but it does not provide specific hyperparameters (e.g., learning rate, batch size, epochs) or detailed system-level training settings. |