ABA+G

Note: Basic knowledge of argumentation theory is assumed throughout this documentation. If you are unfamiliar with this it's recommended that you read the material in the useful reading section before continuing.

Assumption Based Argumentation with goals and preferences (ABA+G) is a newly proposed formalism for structured argumentation.

This project provides an interface which implements reasoning using the ABA+G formalism. There are algorithms for calculating extensions of these frameworks under various semantics, a problem which is of interest in fields such as AI, Law and Medicine.

CLI Usage

First install the relevant dependecies by running the command make freeze && pip install -r requirements.txt. Then install the CLI tool by running pip install .

Users can specify the components of an ABA+G framework (i.e. Assumptions, Rules, Contraries, Preferences) in a file as follows: (Note that goals are as of now still unimplemented)

• myAsm(a). specifies that a is an assumption;
• contrary(a, x). specifies that x is the contrary of assumption a;
• myRule(h, [b1]).** specifies that h <- b1 is a rule;
• myPrefLT(b, a). specifies that assumption a is strictly preferred over assumption b;
• myPrefLE(b, a). specifies that assumption a is strictly or equally preferred over assumption b;

Using the deployed version

A live version of the ABA+ app supporting ABA+G for reasoning with clinical guidelines is available as an API on http://aba-plus-g.herokuapp.com/. Currently the following endpoints are accessible:

• /genereate_explanations is an endpoint which accepts a post request containing a valid DSS JSON object and returns the relevant extensions and accompanying explanations for which actions need to be taken in the treatment of patients.

Useful reading

The following papers provide a background on argumentation theory and this project.

• Francesca Toni, 2014: A tutorial on assumption-based argumentation. A great introductory tutorial to Assumption Based Argumentation (ABA).
• Kristijonas Cyras, Tiago Oliveira, 2019: Resolving Conflicts in Clinical Guidelines using Argumentation. The paper which introduces the ABA+G formalism.

Developing

The project comes with a set of commands you can use to run common operations for your stack:

• make install: Installs run time dependencies.
• make install-dev: Installs dev dependencies together with run time dependencies.
• make freeze: Freezes dependencies from setup.py to requirements.txt (including transitive ones).
• make lint: Runs static analysis.
• make component: Runs component tests.
• make coverage: Runs all tests collecting coverage.
• make test: Runs lint and component.

Updating dependencies

Dependencies are managed from the setup.py file and then frozen in requirements.txt (including transitive dependencies) using make freeze (which uses pip-compile). If you want to change dependencies, you will have to do it in setup.py and then regenerate them using make freeze.

Imagine your setup.py looks like

install_requires = [  
 'pytest~=3.3.0',]  

if you want to update pytest to use version 3.4, you need to change it to

install_requires = [  
 'pytest~=3.4.0',]  

If you execute make freeze, it will regenerate the requirements.txt file with the new dependencies according to what the new version of pytest needs.

Deployment to Heroku

The Flask API available in src/app.py can easily be deployed to Heroku. Please refer to the Procfile in the root folder and the official Heroku documentation for more information on how to do this.

Contributing

If you would like to contribute to the project please follow these steps:

• Clone the project locally.
• Create a new feature branch.
• Implement your feature while adhering to the development practices outlined above.
• Once you are happy with your changes, make sure that the TravisCI checks are passing and create a pull request with a description of what you have changed.

Your pull request will be peer reviewed and merged into the master branch once ready.