Before AI learned from massive datasets, many systems worked with explicit knowledge.

Facts.

Rules.

Inference.

That is the core of Knowledge-Based AI.

Core Idea

Knowledge-Based AI stores knowledge in a structured form.

Then it uses rules to derive new conclusions.

The system does not “learn” from data in the modern deep learning sense.

It reasons over what it already knows.

That makes the structure very different from machine learning.

The Key Structure

A simple Knowledge-Based AI system looks like this:

Knowledge Base → Rules → Inference Engine → Conclusion

Or more compact:

Knowledge-Based AI = Facts + Rules + Inference

The knowledge base stores information.

The rule system defines how conclusions can be derived.

The inference engine applies those rules.

Implementation View

At a high level, a rule-based system works like this:

store known facts

store IF-THEN rules

compare facts with rule conditions

apply matching rules

generate new facts or conclusions

repeat until no useful rule…