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

Related tags

Deep Learning machine-learning recommendation-system recommender-system mhealth ehealth game-th
Overview

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;
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