[ENH] add docs: ABL overview intro

docs/Brief-Introduction/Components.rst

@@ -1,4 +1,4 @@
-ABL-Package Components
+Components
 ==================
 
 .. contents:: Table of Contents

docs/Overview/Abductive Learning.rst

@@ -1,5 +1,27 @@
 Abductive Learning
 ==================
 
-.. contents:: Table of Contents
+Traditional supervised machine learning, e.g. classification, is predominantly data-driven. Here, a set of training examples \left\{\left(x_1, y_1\right), \ldots,\left(x_m, y_m\right)\right\} is given, where x_i \in \mathcal{X} is the i-th training instance, y_i \in \mathcal{Y} is the corresponding ground-truth label. These data are then used to train a classifier model  f: \mathcal{X} \mapsto \mathcal{Y}  to accurately predict the unseen data.
+
+（可能加一张图，比如左边是ML，右边是ML+KB）
+
+In Abductive Learning (ABL), we assume that, in addition to data as examples, there is also a knowledge base \mathcal{KB} containing domain knowledge at our disposal. We aim for the classifier  f: \mathcal{X} \mapsto \mathcal{Y}  to make correct predictions on unseen data, and meanwhile, the logical facts grounded by \left\{f(\boldsymbol{x}_1), \ldots, f(\boldsymbol{x}_m)\right\} should be compatible with \mathcal{KB}.
+
+The process of ABL is as follows: 
+
+1. Upon receiving data inputs \left\{x_1,\dots,x_m\right\}, pseudo-labels \left\{f(\boldsymbol{x}_1), \ldots, f(\boldsymbol{x}_m)\right\} are obtained, predicted by a data-driven classifier model. 
+2. These pseudo-labels are then converted into logical facts \mathcal{O} that are acceptable for logical reasoning. 
+3. Conduct joint reasoning with \mathcal{KB} to find any inconsistencies. 
+4. If found, the logical facts contributing to minimal inconsistency can be identified and then modified through abductive reasoning, returning modified logical facts \Delta(\mathcal{O}) compatible with \mathcal{KB}. 
+5. These modified logical facts are converted back to the form of pseudo-labels, and used for further learning of the classifier. 
+6. As a result, the classifier is updated and replaces the previous one in the next iteration. 
+
+This process is repeated until the classifier is no longer updated, or the logical facts \mathcal{O} are compatible with the knowledge base. 
+
+The following figure illustrates this process:
+
+一张图
+
+We can observe that in the above figure, the left half involves machine learning, while the right half involves logical reasoning. Thus, the entire abductive learning process is a continuous cycle of machine learning and logical reasoning. This effectively form a dual-driven (data & knowledge driven) learning system, integrating and balancing the use of machine learning and logical reasoning in a unified model.
+
 

docs/Overview/Introduction.rst

@@ -1,12 +0,0 @@
-A Brief Introduction to ABL-Package
-==================
-
-.. contents:: Table of Contents
-
-ABL-Package Components
-----------------------------------------
-
-
-
-Use ABL-Package step by step
-------------------------------

docs/README.rst

@@ -1,16 +1,18 @@
 ABL-Package
-^^^^^^
+===========
 
-ABL(Abductive Learning) is ...
+ABL-Package is an open source library for Abductive Learning that supports building a model leveraging information from both data and (logical) domain knowledge. Using ABL-Package, users may form a dual-driven (data & knowledge driven) learning system, integrating and balancing the use of machine learning and logical reasoning in a unified model.
+
+插一张图片
 
 Installation
--------------
+------------
 
 ABL is distributed on PyPI and can be installed with ``pip``:
 
 .. code:: console
 
-   $ pip install ABL
+   $ pip install abl
 
 Alternatively, to install ABL by source code, download this project and sequentially run following commands in your terminal/command line.
 

docs/index.rst

@@ -10,7 +10,7 @@
 
 .. toctree::
    :maxdepth: 2
-   :caption: A Brief Introduction to ABL-Package 
+   :caption: A Brief Introduction
 
    Brief-Introduction/Components
    Brief-Introduction/Usage