ABLkit/docs/Overview/Abductive-Learning.rst

Abductive Learning
==================

Traditional supervised machine learning, e.g. classification, is
predominantly data-driven, as shown in the below figure. 
Here, a set of data examples is given, including training instances 
:math:`\{x_1,\dots,x_m\}` and corresponding ground-truth labels :math:`\{\text{label}(x_1),\dots,\text{label}(x_m)\}`. 
These data are then used to train a classifier model :math:`f`, 
aiming to accurately predict the unseen data instances.

.. image:: ../_static/img/ML.png
   :align: center
   :width: 280px

In **Abductive Learning (ABL)**, we assume that, in addition to data, 
there is also a knowledge base :math:`\mathcal{KB}` containing
domain knowledge at our disposal. We aim for the classifier :math:`f` 
to make correct predictions on data instances :math:`\{x_1,\dots,x_m\}`, 
and meanwhile, the pseudo-groundings grounded by the prediction
:math:`\left\{f(\boldsymbol{x}_1), \ldots, f(\boldsymbol{x}_m)\right\}`
should be compatible with :math:`\mathcal{KB}`.

The process of ABL is as follows:

1. Upon receiving data instances :math:`\left\{x_1,\dots,x_m\right\}` as input,
   pseudo-labels
   :math:`\left\{f(\boldsymbol{x}_1), \ldots, f(\boldsymbol{x}_m)\right\}`
   are predicted by a data-driven classifier model.
2. These pseudo-labels are then converted into pseudo-groundings
   :math:`\mathcal{O}` that are acceptable for logical reasoning.
3. Conduct joint reasoning with :math:`\mathcal{KB}` to find any
   inconsistencies. If found, the pseudo-groundings that lead to minimal 
   inconsistency can be identified.
4. Modify the identified facts through **abductive reasoning** (or, **abduction**), 
   returning revised pseudo-groundings :math:`\Delta(\mathcal{O})` which are
   compatible with :math:`\mathcal{KB}`.
5. These revised pseudo-groundings are converted back to the form of
   pseudo-labels, and used like ground-truth labels in conventional 
   supervised learning to train a new classifier.
6. The new classifier will then be adopted to replace the previous one
   in the next iteration.

This above process repeats until the classifier is no longer updated, or
the pseudo-groundings :math:`\mathcal{O}` are compatible with the knowledge
base.

The following figure illustrates this process:

.. image:: ../_static/img/ABL.png
   :width: 800px

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 forms a paradigm that
is dual-driven by both data and domain knowledge, integrating and
balancing the use of machine learning and logical reasoning in a unified
model.

For more information about ABL, please refer to `References <../References.html>`_.

.. _abd:

.. admonition:: What is Abductive Reasoning?

   Abductive reasoning, also known as abduction, refers to the process of
   selectively inferring certain facts and hypotheses that explain
   phenomena and observations based on background knowledge. Unlike
   deductive reasoning, which leads to definitive conclusions, abductive
   reasoning may arrive at conclusions that are plausible but not conclusively
   proven.

   In ABL, given :math:`\mathcal{KB}` (typically expressed
   in first-order logic clauses), one can perform both deductive and 
   abductive reasoning. Deductive reasoning allows deriving
   :math:`b` from :math:`a`, while abductive reasoning allows inferring
   :math:`a` as an explanation of :math:`b`. In other words, 
   deductive reasoning and abductive reasoning differ in which end, 
   right or left, of the proposition “:math:`a\models b`” serves as conclusion.