https://github.com/ivandonadello/aggregated-policies-biparty-decision-theory

https://github.com/ivandonadello/aggregated-policies-biparty-decision-theory

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• Host: GitHub
• URL: https://github.com/ivandonadello/aggregated-policies-biparty-decision-theory
• Owner: ivanDonadello
• Created: 2021-12-21T08:35:33.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-07-28T13:43:34.000Z (6 months ago)
• Last Synced: 2024-07-28T15:03:58.973Z (6 months ago)
• Language: Python
• Size: 21.6 MB
• Stars: 0
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Aggregated-Policies-Biparty-Decision-Theory

This repository contains the source code for the paper "Compromises in Dialogical Argumentation: Aggregated Policies for Biparty Decision Theory" published at the 27th European Conference on Artificial Intelligence (ECAI 2024).

## Structure

- `src` that contains the source Python code;

- `data` that contains the benchmark data, see the relative `README` for downloading the data.

- `results` contains the results for the performance of the policies;

- `settings.py` contains the main settings for the experiments as described in the paper below;

## Requirements

The following Python packages are required:

-   [numpy](http://www.numpy.org/) tested with version 1.25.0;

-   [pandas](https://pandas.pydata.org/) tested with version 2.0.2.

-   [matplotlib](https://matplotlib.org/) tested with version 3.7.1;

-   [seaborn](https://seaborn.pydata.org/) tested with version 0.12.2.

-   [pygraphviz](https://pypi.org/project/pygraphviz/) tested with version 1.7.0.

### Running the code

To run the all the experiments (Load trees, propagate utilities and generate all metrics in the paper)

for both datasets (**don2022** or **don2022NoOPT**), use:

```

python3 run_experiments.py --ds=don2022

```

or

```

python3 run_experiments.py --ds=don2022NoOPT

```

[//]: # (To run the policy experiments for a given dataset (don2022 or don2022NoOPT), type:)

[//]: # (```)

[//]: # (python3 run_policy_experiments.py --ds=don2022)

[//]: # (```)

[//]: # (or)

[//]: # (```)

[//]: # (python3 run_policy_experiments.py --ds=don2022NoOPT)

[//]: # (```)

[//]: # ()

[//]: # (To run the metrics evaluation, type:)

[//]: # (```)

[//]: # (python3 run_policy_experiments.py --ds=don2022)

[//]: # (```)

[//]: # (for the don2022 dataset or)

[//]: # (```)

[//]: # (python3 run_policy_experiments.py --ds=don2022NoOPT)

[//]: # (```)

[//]: # (for the don2022NoOPT dataset. )

### Results

After running the experiments, the results are listed in the `results` folder. All tables described in the paper

can be found in this folder as csv files for each `dataset` `(don2022 and don2022NoOPT)`:

- `policies_experiments_{dtaset}.csv`: contains the results after running `run_policy_experiments.py` each tree in `\data\DT` with the given 

policies.

- `metrics_{metric}_{dataset}.csv`: contains the results for the metrics `(AAD, APU, AOU)` by running `run_metrics_experiments.py` for each `policies_experiments` in `results` folder.

- `trees_metrics.csv`: table with metrics for all Decision trees in `\data\DT`.

- `Appendices.pdf`: contains the appendices referred in the paper.

### Citing

Please use the following `.bib` entry to cite our work

```

TO APPEAR

```