Expert Systems

An expert system is a program that copies a human specialist's decision process in one domain.

It has two parts. The knowledge base stores facts and rules, usually as if-then statements written by experts. The inference engine applies those rules to reach conclusions, including under uncertainty, missing facts, and long logical chains. Expert systems were the main AI architecture in the 1980s and still run in production. In healthcare they flag drug interactions and suggest tests.

In finance they score credit risk and catch fraud. In manufacturing they walk technicians through broken machines.

The knowledge base can be updated when research changes, without rewriting the whole program. These systems are less flexible than modern neural networks. They are easier to inspect, because each recommendation has a traceable chain of rules.

If-then rules are readable. An inference engine fires them in chains, including when some facts are missing. That was the 1980s default for AI products. Drug-interaction checkers, credit and fraud rules, and machine troubleshooting trees still look like this. When a paper changes a guideline, you add or edit rules instead of retraining a net. Neural models flex more on messy inputs.

Expert systems still win when an auditor must see the exact chain that produced a recommendation. MYCIN (1970s) was a medical expert system of hand-written rules. Deep learning later replaced most of that style, but the goal (encode specialist knowledge) is the same.