Symbolic AI systems are based on the manipulation of symbols and logical rules for knowledge representation and problem solving.

Typical methods include knowledge bases, logical inference, and expert systems.

Unlike data-driven approaches such as machine learning, symbolic systems are explainable and follow explicit rules.

They are particularly suitable for tasks where knowledge must be structured and transparently represented, such as in planning or diagnosis.

Challenges lie in scalability and dealing with uncertain or incomplete knowledge.