Depending on your context, “explainer-based” usually refers to either Explanation-Based Learning (EBL) in artificial intelligence, or Explainer-Based Visual Analytics used to diagnose complex black-box machine learning models. 1. Explanation-Based Learning (EBL) in AI
In traditional machine learning, systems need thousands of examples to recognize a pattern (inductive learning). In contrast, Explanation-Based Learning (EBL) is a technique where an AI learns a generalized rule from just one or a few training examples by leveraging an existing background theory.
How it works: The AI looks at a single successful instance, uses its pre-existing “domain knowledge” to construct a logical explanation of why the instance succeeded, and generalizes that specific explanation into a permanent rule.
The Chess Example: If a computer watches a grandmaster execute a specific checkmate, an inductive AI would need to see that exact sequence hundreds of times. An EBL-based AI uses its knowledge of chess rules to analyze the single match, explain why the opponent couldn’t escape, and instantly create a generalized strategy for future games. Core Components: Target Concept: What the AI is trying to learn. Training Example: The single scenario or instance observed.
Domain Theory: The set of hard rules and logical facts the AI already knows.
Operationality Criteria: The constraints that ensure the newly learned rule is actually easy for the computer to execute later. 2. Explainer-Based Frameworks in Explainable AI (XAI)
When engineers talk about “explainer-based diagnostics,” they are referring to specialized tools used to open up the “black box” of complex systems like Deep Neural Networks or Random Forests.
An explainer is an auxiliary algorithm or wrapper application built strictly to interpret and translate the logic of a primary AI model into human-readable terms.