Symbolic AI
An approach to AI that represents knowledge using symbols and rules, and reasons by manipulating those symbols logically. Symbolic AI dominated before the deep learning era.
Why It Matters
Symbolic AI provides interpretable, provably correct reasoning — qualities that neural AI struggles with. The field is seeing a resurgence in neuro-symbolic AI approaches.
Example
An expert system using rules like 'IF temperature > 38°C AND cough = true AND fatigue = true THEN diagnosis = possible_flu' — explicit, traceable logic.
Think of it like...
Like a detective who writes down clues on a whiteboard and follows logical rules to reach conclusions — every step of reasoning is explicit and verifiable.
Related Terms
Expert System
An early AI system that mimics human expertise in a specific domain using a knowledge base of rules and facts. Expert systems were the dominant AI approach in the 1980s.
Knowledge Graph
A structured representation of real-world entities and the relationships between them, stored as a network of nodes (entities) and edges (relationships). Knowledge graphs capture factual information in a machine-readable format.
Neural Network
A computing system inspired by the biological neural networks in the human brain. It consists of interconnected nodes (neurons) organized in layers that process information and learn to recognize patterns.
Neuro-Symbolic AI
Approaches that combine neural networks (pattern recognition, learning from data) with symbolic AI (logical reasoning, knowledge representation) to get the strengths of both.