Explanation Based Learning in Artificial Intelligence

Explanation-Based Learning in AI

When a learning system is explanation-based, it takes an example and explains the lessons it takes away from it. The Explanation-based system just uses the training's relevant components. This explanation is converted into a specific format that can be comprehended by a solution program. To make this explanation applicable to various situations, it has been generalized.


In artificial intelligence, explanation-based learning is a technique for solving problems that involve agent learning through the analysis of particular scenarios and their correlation with previously learned data. In addition, the agent uses his newfound knowledge to address related problems. EBL algorithms use logical thinking and domain knowledge in addition to statistical analysis to find patterns and make predictions.


In artificial intelligence, explanation-based learning is a subfield of machine learning that concentrates on developing algorithms that pick up knowledge from problems that have already been resolved. This approach to problem-solving is particularly useful for handling complex, complex issues that demand a deep understanding of the underlying mechanisms.

EBL System Schematic

The schematic below shows explanation-based learning:

explanation-based-learning-in-artificial-intelligence

Explanation-based learning accepts four kinds of inputs:

1.  A training example

This represents what the learning observes in reality.


2.  A goal concept

It provides an overview of the knowledge that the program is expected to acquire.


3.  An operational criterion

It is an explanation of the applicable concepts.


4. A domain theory

It is a collection of guidelines that explains how things relate to one another in a domain. 

A generalization of the training example that is adequate to both define the goal idea and meet the operational condition is produced by explanation-based learning machines. 

This includes the how-to steps:

  • Justification: To make the training example more in line with the aim concepts, all unnecessary elements are removed using the domain theory.

  • Generalization: To preserve the aim notions, the explanation is made as general as feasible.

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