Learn the Basics || Quick Start || Dataset & Data Structure || Learning Part || Reasoning Part || Evaluation Metrics || Bridge

Learn the Basics

Modules in ABLkit

ABLkit is an efficient toolkit for Abductive Learning (ABL), a paradigm which integrates machine learning and logical reasoning in a balanced-loop. ABLkit comprises three primary parts: Data, Learning, and Reasoning, corresponding to the three pivotal components of current AI: data, models, and knowledge. Below is an overview of the ABLkit.

Data part efficiently manages data storage, operations, and evaluations. It includes the ListData class, which defines the data structures used in ABLkit, and comprises common data operations like insertion, deletion, retrieval, slicing, etc. Additionally, it contains a series of evaluation metrics such as SymbolAccuracy and ReasoningMetric (both specialized metrics inherited from the BaseMetric class), for evaluating performance from a data perspective.

Learning part focuses on creating, training, and utilizing machine learning models. The ABLModel class is the central class that encapsulates the machine learning model. This class is compatible with various frameworks, including those based on scikit-learn or PyTorch neural networks constructed by the BasicNN class.

Reasoning part concentrates on constructing domain knowledge and performing reasoning. The KBBase class allows users to customize a domain knowledge base. For diverse types of knowledge, we also offer implementations like GroundKB and PrologKB (both inherited from the KBBase class). The latter, for instance, imports knowledge bases via Prolog files. Upon building the knowledge base, the Reasoner class is responsible for minimizing the inconsistency between the knowledge base and data.

The integration of these three parts is achieved through the Bridge part, which features the SimpleBridge class (derived from the BaseBridge class). The Bridge part synthesizes data, learning, and reasoning, facilitating the training and testing of the entire ABL framework.

Use ABLkit Step by Step

In a typical ABL process, as illustrated below, data inputs are first predicted by the learning model ABLModel.predict, and the outcomes are pseudo-labels. These labels then pass through deductive reasoning of the domain knowledge base KBBase.logic_forward to obtain the reasoning result. During training, alongside the aforementioned forward flow (i.e., prediction –> deduction reasoning), there also exists a reverse flow, which starts from the reasoning result and involves abductive reasoning KBBase.abduce_candidates to generate possible revised pseudo-labels. Subsequently, these pseudo-labels are processed to minimize inconsistencies with the learning part. They in turn revise the outcomes of the learning model, which are then fed back for further training ABLModel.train.

To implement this process, the following five steps are necessary:

1. Prepare datasets

   Prepare the data’s input, ground truth for pseudo-labels (optional), and ground truth for reasoning results.

2. Build the learning part

   Build a machine learning base model that can predict inputs to pseudo-labels. Then, use ABLModel to encapsulate the base model.

3. Build the reasoning part

   Define a knowledge base by building a subclass of KBBase, specifying how to process pseudo-labels to reasoning results. Also, create a Reasoner for minimizing inconsistencies between the knowledge base and data.

4. Define evaluation metrics

   Define the metrics by building a subclass of BaseMetric. The metrics will specify how to measure performance during the training and testing of the ABL framework.

5. Bridge learning and reasoning

   Use SimpleBridge to bridge the learning and reasoning part for integrated training and testing.