Expert System

A computer system that emulates the decision-making ability of a human expert.

How it works

An expert system has two main components: a knowledge base containing facts and rules encoded by domain experts (usually in if-then form), and an inference engine that applies those rules to a given set of inputs to reach conclusions. Forward chaining starts from known facts and applies rules to derive new facts. Backward chaining starts from a goal and works backwards to find supporting facts. Expert systems require extensive manual knowledge engineering to build and maintain.

Why it matters

Expert systems were the dominant commercial AI technology of the 1980s and 1990s, powering medical diagnosis systems, financial advisors, and industrial fault detection tools. While they have largely been supplanted by machine learning in domains with plentiful data, they remain valuable in highly regulated domains requiring traceable, auditable reasoning — such as medical device approval and legal compliance — where a black-box neural network's output cannot be justified without a human-readable chain of reasoning.