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What is Expert System?

An expert system is a computer program that emulates the decision-making ability of a human expert in a narrow domain by applying a collection of if-then rules to known facts.

It is built around two core components: a knowledge base that stores domain facts and production rules, and an inference engine that chains those rules together to reach conclusions or recommendations.

Unlike data-driven machine learning models, expert systems rely on explicitly encoded human knowledge rather than patterns learned from examples, making their reasoning transparent and easy to audit.

They typically include an explanation facility that can trace which rules fired, helping users understand why a particular conclusion was reached.

Example

MYCIN was an early medical expert system that used about 600 rules to diagnose bacterial infections and recommend antibiotics based on patient symptoms and lab results.

Why it matters

Although largely replaced by machine learning for many tasks, the rule-based, explainable approach of expert systems still influences modern decision-support tools, regulatory compliance systems, and efforts to make AI reasoning more interpretable.

Frequently asked questions

Expert systems use hand-crafted rules and facts provided by humans, while machine learning learns patterns automatically from data.