# explanation-based learning

> form of machine learning

**Wikidata**: [Q133580](https://www.wikidata.org/wiki/Q133580)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Explanation-based_learning)  
**Source**: https://4ort.xyz/entity/explanation-based-learning

## Summary
Explanation-based learning (EBL) is a form of machine learning that uses domain-specific knowledge to generalize from examples. Unlike other approaches, EBL relies on explicit explanations to derive new concepts, making it particularly useful in domains where human expertise is available.

## Key Facts
- A subclass of machine learning, focusing on generalization through explanations
- Also known as EBL and Apprentissage basé sur l'explication
- Shortened to EBL in some contexts
- Freebase ID: /m/05mtw5w
- Microsoft Academic ID (discontinued): 2778972135
- Primarily studied in English and French Wikipedia versions
- Part of the broader field of machine learning, which involves algorithms and statistical models for task automation

## FAQs
### Q: What is the difference between explanation-based learning and other machine learning methods?
A: Unlike data-driven methods like neural networks, EBL relies on explicit domain knowledge to generalize from examples, rather than learning patterns from raw data.

### Q: In which languages is explanation-based learning documented?
A: The concept is documented in English and French Wikipedia articles.

### Q: What is the Freebase ID for explanation-based learning?
A: The Freebase ID is /m/05mtw5w.

## Why It Matters
Explanation-based learning is significant in machine learning because it leverages human expertise to improve generalization. By using domain-specific knowledge, EBL can derive more accurate and interpretable models compared to purely data-driven approaches. This makes it particularly valuable in fields like robotics, medical diagnosis, and natural language processing, where human understanding of the domain is crucial. While EBL has been less widely adopted than deep learning, it remains a key area of study for developing more transparent and efficient AI systems.

## Notable For
- Being a specialized form of machine learning that relies on explicit explanations
- Having a shorter name (EBL) in some contexts
- Being documented in two Wikipedia languages (English and French)
- Having a discontinued Microsoft Academic ID (2778972135)
- Being part of the broader machine learning field, which includes algorithms and statistical models

## Body
### Definition and Classification
Explanation-based learning is a specialized approach within machine learning that focuses on generalization through the use of domain-specific knowledge. It is distinct from other machine learning methods, such as neural networks, which rely on pattern recognition from large datasets.

### Naming and Identification
The term is also referred to as EBL or Apprentissage basé sur l'explication. It is linked to the Freebase ID /m/05mtw5w and had a discontinued Microsoft Academic ID of 2778972135.

### Documentation and Availability
The concept is documented in Wikipedia in both English and French, reflecting its relevance in multilingual academic and technical contexts. The English Wikipedia page is titled "Explanation-based learning."

### Relationship to Machine Learning
As a subclass of machine learning, EBL is part of the broader scientific study of algorithms and statistical models that enable computer systems to perform tasks without explicit instructions. This relationship highlights its role in advancing automated reasoning and knowledge representation.