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 Swarm Behaviors using Grammatical Evolution and Behavior Trees
Authors: Aadesh Neupane, Michael Goodrich
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically verify the algorithm s effectiveness on three different problems: single-source foraging, collective transport, and nest maintenance. |
| Researcher Affiliation | Academia | Aadesh Neupane and Michael Goodrich Brigham Young University, Provo, UT EMAIL, EMAIL |
| Pseudocode | No | The paper provides a BNF grammar for the swarm behaviors but does not include pseudocode or an explicitly labeled algorithm block for the GEESE-BT algorithm itself. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the described methodology. |
| Open Datasets | No | The paper describes simulated environments for foraging, collective transport, and nest maintenance, but it does not specify the use of a named public dataset or provide access information (link, DOI, formal citation) for any custom-created dataset. |
| Dataset Splits | No | The paper does not explicitly provide specific dataset split information (percentages, sample counts, or detailed methodology) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'a python-based BT implementation' but does not provide specific version numbers for Python or any other software libraries or solvers used in the experiments. |
| Experiment Setup | Yes | Parameters GEESE Number of Genomes Required to Trigger Genetic Operations, Parent-Selection Fitness + truncation, Elite-size 1, Mutation Probability 0.01, Crossover variable onepoint, Crossover Probability 0.9, Genome-Selection Diversity, Maximum Codon Int 1000, Number of Agents 100, Behavior Sample 0.1 (Table 1: GEESE parameters used for the swarm experiments). |