Source Code - A Robust Decision-Making Framework Based on Collaborative Agents
softwareposted on 11.08.2020 by Johana Florez-Lozano, Fabio Caraffini, Carlos Parra, Mario Gongora
Code as a research output can either be uploaded directly from your computer or through the code management system GitHub. Versioning of code repositories is supported.
This research item contains an archive with source code and data for reproducing the results reported in the artcile "A Robust Decision-Making Framework Based on Collaborative Agents".
To run the attached Python code you need the following libraries to be installed: osbrain, numpy, neat, gzip, pickle, skfuzzy, skimage, PIL, scipy.
To produce results, go though the following steps:
1. generate the feed-forward artificial neural networks (ffANN) and genetic fuzzy system (GFS) with the file "Train";
2. copy the produced files relative to the ffANN in file "Net" and the GFS files to the "FS" file, both inside the file "MAS_execution".
3. select the number of agents in the main file of execution (run_process);
4. check the file "AgentX" to modify the source of the information.
5. And finally, you can execute the system running the "run_process" file, each agent will execute as a subprocess.
Inside this folter there are two intelligent methods, GFS and NEAT.
*In GFS, you can train modifing file "execution". In this file you can edit the database and the number of agents.
*In NEAT, You can train with "training_Neat". The training parameters configuration per data set are adjustables on files "config-CX"
Both folders has data from four datasets of example.
In the folder "Data" and "Test" the system is going to save information of the result system.
The files "definitions", "Features", "GFS_Agents" are libraries that the MAS use.