Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees
Authors: Aadesh Neupane, Michael Goodrich
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 aadeshnpn@byu.edu, mike@cs.byu.edu |
| 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). |