Machine Learning for Argument-Based Computational Persuasion

This repo contains the source code and a benchmark for predicting user's utilities with Machine Learning techniques for Computational Persuasion

CODE WILL BE SOON AVAILABLE

Requirements

The following packages are required:

• numpy, tested with version 1.19.5
• matplotlib, tested with version 3.3.4
• sklearn, tested with version 0.24.1
• pandas, tested with version 1.1.5

Usage

Type:

 $ python3 experiments_util_prediction_parallel.py -p ${parallelism_flag}

where ${parallelism_flag} can be True or False whether you want to run the experiments using all the available CPUs in your machine.

To run the simulations. Type:

 $ python3 meat_example_experiments.py

to run the experiment with the red meat case study.

Files

• data/DT contains the decision trees of the simulations;
• data/datasets contains the datasets of the simulations;
• results contains the results of the simulations;
• results/tree_samples contains the results for each tree of the simulations;
• meat_data contains the input data for the red meat case study;
• meat_results contains the results for the red meat case study;