ABLkit/docs/README.rst

ABL Kit
=======

**ABL Kit** is an efficient Python toolkit for **Abductive Learning (ABL)**.
ABL is a novel paradigm that integrates machine learning and 
logical reasoning in a unified framework. It is suitable for tasks
where both data and (logical) domain knowledge are available. 

.. image:: _static/img/ABL.png

Key Features of ABL Kit:

- **Great Flexibility**: Adaptable to various machine learning modules and logical reasoning components.
- **User-Friendly**: Provide **data**, :blue-bold:`model`, and :green-bold:`KB`, and get started with just a few lines of code.
- **High-Performance**: Optimization for high accuracy and fast training speed.

ABL Kit encapsulates advanced ABL techniques, providing users with
an efficient and convenient toolkit to develop dual-driven ABL systems,
which leverage the power of both data and knowledge.

.. image:: _static/img/ABLKit.png

Installation
------------

Install from PyPI
^^^^^^^^^^^^^^^^^

The easiest way to install ABL Kit is using ``pip``:

.. code:: bash

    pip install ablkit

Install from Source
^^^^^^^^^^^^^^^^^^^

Alternatively, to install from source code, 
sequentially run following commands in your terminal/command line.

.. code:: bash

    git clone https://github.com/AbductiveLearning/ABLKit.git
    cd ABLKit
    pip install -v -e .

(Optional) Install SWI-Prolog
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

If the use of a :ref:`Prolog-based knowledge base <prolog>` is necessary, the installation of `SWI-Prolog <https://www.swi-prolog.org/>`_ is also required:

For Linux users:

.. code:: bash

    sudo apt-get install swi-prolog

For Windows and Mac users, please refer to the `SWI-Prolog Install Guide <https://github.com/yuce/pyswip/blob/master/INSTALL.md>`_.

References
----------

For more information about ABL, please refer to: `Zhou, 2019 <http://scis.scichina.com/en/2019/076101.pdf>`_ 
and `Zhou and Huang, 2022 <https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf>`_.

.. code-block:: latex

    @article{zhou2019abductive,
        title     = {Abductive learning: towards bridging machine learning and logical reasoning},
        author    = {Zhou, Zhi-Hua},
        journal   = {Science China Information Sciences},
        volume    = {62},
        number    = {7},
        pages     = {76101},
        year      = {2019}
    }

    @incollection{zhou2022abductive,
        title     = {Abductive Learning},
        author    = {Zhou, Zhi-Hua and Huang, Yu-Xuan},
        booktitle = {Neuro-Symbolic Artificial Intelligence: The State of the Art},
        editor    = {Pascal Hitzler and Md. Kamruzzaman Sarker},
        publisher = {{IOS} Press},
        pages     = {353--369},
        address   = {Amsterdam},
        year      = {2022}
    }