Explanation-based learning (EBL) is a form of learning in artificial intelligence (AI) that involves understanding the underlying domain or problem space and generating explanations for specific instances. Unlike other machine learning approaches that rely on numerous examples, EBL focuses on a deep understanding of individual examples, generating explanations based on underlying principles, and generalizing those explanations to cover other related instances.

Here’s a more detailed overview of EBL:

1. Analyzing the Problem: EBL starts by analyzing a particular instance of a problem, applying domain knowledge to understand why a specific solution works.
2. Generating an Explanation: Based on the analysis, EBL generates an explanation of why the solution is correct. This explanation leverages domain knowledge, logical reasoning, and underlying principles to articulate why the solution makes sense.
3. Generalizing the Explanation: The explanation is then generalized to create a rule or a pattern that can be applied to other similar instances. This generalization allows the system to learn from a single example and apply the learning to many related cases.
4. Storing the Generalized Rule: The generalized rule is stored and used to solve similar problems in the future. By having this rule, the system can quickly solve related problems without having to analyze each one individually.
5. Integration with Other Learning Techniques: EBL can be integrated with other learning techniques, such as inductive learning and analogical reasoning, to form more comprehensive and robust learning models.

The advantage of EBL is that it can provide a deep understanding of specific problems, allowing for intelligent generalization to related instances. However, it requires significant domain knowledge and logical reasoning capabilities, making it a more complex approach than many other machine learning techniques.

EBL has been applied to various domains, such as natural language processing, expert systems, and robotics, allowing AI systems to learn more efficiently and effectively from individual examples.

This learning methodology emphasizes the importance of explanations and understanding, aligning with human-like learning processes, and potentially making the AI models more interpretable and trustworthy.