Explanationbased

Explanation-based is commonly used as an abbreviation or a keyword referring to explanation-based learning (EBL), a form of knowledge-based learning that was introduced in the early 1990s within the artificial intelligence and machine learning communities. EBL derives its power from the observation that many learning tasks involve recurring patterns that can be captured by explicit logical explanations. The algorithm uses a single example and an existing general knowledge base to perform a deductive reasoning process to generate a generalized rule that covers the illustrated case. This rule is then generalized further to accommodate a broader set of instances, leading to a rapid learning speed with minimal training data compared to purely data‑driven statistical approaches.

The roots of explanation-based learning lie in logic programming and inference engines such as Prolog. Researchers at the University of Auckland, including Professor Pearl, emphasised that problems solvable by backward chaining could be turned into explanation constructs, which in turn could be reused for unseen instances. Subsequent developments integrated EBL with support vector machines and Markov logic networks, creating hybrid systems that preserve the interpretability of logic with the predictive power of statistical models.

Despite its efficiency on structured domains, explanation-based learning faces challenges such as the requirement for a comprehensive prior knowledge base, vulnerability to noise, and difficulty scaling to domains with highly disconnected feature spaces. Contemporary work focuses on combining explanation-based methods with deep neural representations, proposing neural-symbolic systems that extract symbolic explanations from learned embeddings. This hybrid approach seeks to maintain explainability while harnessing the representational capacity of modern machine learning.