Deep Learning for Abstract Argumentation

Neuro-symbolic approaches to reasoning problems from abstract argumentation

This repository holds the code to produce data and experiments for using learning-based approaches to symbolic reasoning problems of static and dynamic abstract argumentation. The work is described in the following publications:

• Deep Learning for Abstract Argumentation Semantics (IJCAI 2020) (previous code)
• Enforcement Heuristics for Argumentation with Deep Reinforcement Learning (AAAI 2022)

Please email me for questions.

Installation

Create a virtual python 3 environment (tested with 3.8) and install with pip or conda

Pip

First install

• pytorch (tested with version 1.11)
• pytorch_geometric (tested with version 2.0.2)

seperately with the cuda/cpu settings for your system, then install the other requirements with

pip install -r requirements.txt

Conda

Alternatively use the conda_environment.yaml with conda.

AF generators

Afterwards go to the src/data/generators/vendor directory and compile the Argumentation Framework generators with ./install.sh (compiling requires Java, Ant and Maven).

Tests

Optionally test the installation by running the pytest unit tests in tests/test_components.py

Project structure

├── data                generated datasets go here
├── models              trained models go here     
├── src
│   ├── data            scripts to generate datasets         
│   ├── experiment      scripts to run experiments
│   ├── models          machine learning models
├── tests               pytest unit tests

Generate Data

To generate Argumentation Frameworks we use AFBenchGen2 , AFGen and probo (thanks to the original authors!)

The src/data folder contains data generation scripts:

• generate_apx.py generates AFs in apx format.
• apx_to_afs.py converts apx files to ArgumentationFramework objects and computes extensions and argument acceptance
• afs_to_enforcement generates and solves status and extensions enforcement problems for an AF The apx files are

If we want, for instance, to create a dataset for static and dynamic argumentation problems named 'test-25' and consisting of 1000 graphs each containing 25 arguments, execute:

python -m src.data.generate_apxs --name test-25 --min_args 25 --max_args 25 --num 10000
python -m src.data.apxs_to_afs --name test-25
python -m src.data.afs_to_enforcement --name test-25

To see more information about the other parameters for each scriptuse the --help parameter

Run experiments

To track the experiment I use Weights and Biases. Every experiment is defined as an Pytorch Lightning module. Thus you can use all the trainer flags as extra parameters to control the experimental setting.

Supervised learning

Experiments for Deep Learning for Abstract Argumentation Semantics (IJCAI 2020)

Train and evaluate model with python -m src.experiment.supervised.sl_experiment and set the parameters

• --train_ds use the name given to the training set
• --val_ds use the name given to the validation set
• --test_ds use the name given to the test set
• --task choose a task from ['cred','scept','enum']
• --semantics choose a semantics from ['com','stb','prf','grd']
• --model choose a model from ['AGNN','GCN','FM2']
• --tag name this experiment
• --help to see more information about other parameters

Example

python -m src.experiment.supervised.sl_experiment --tag test --train_ds train-10 --val_ds val-10 --test_ds test-10 --task enum --semantics grd --model AGNN

Reinforcement learning

Experiments for Enforcement Heuristics for Argumentation with Deep Reinforcement Learning (AAAI 2022)

Train and evaluate model with python -m src.experiment.reinforcement.rl_experiment and set the parameters

• --train_ds use the name given to the training dataset
• --val_ds use the name given to the validation dataset
• --test_ds use the name given to one or more the test dataset(s)
• --task choose a task from ['strict','non-strict','cred','scept']
• --semantics choose a semantics from ['com','stb','prf','grd']
• --tag name this experiment
• --help to see more information about other parameters

Example

python -m src.experiment.reinforcement.rl_experiment --tag test --train_ds train-10 --val_ds val-10 --test_ds test-10 --task strict --semantics grd --aggregation multi