Symbolic AI

Symbolic AI is an approach to artificial intelligence that uses logic, rules, and symbols to replicate human intellectual reasoning and judgment. Also known as "classical AI" or "rule-based AI," it was the dominant paradigm in AI research from the 1950s through the 1980s and is historically significant as the framework that laid the groundwork for modern AI. In this approach, the world is modeled as "symbols," and logical rules and knowledge bases — such as "If A, then B" — are constructed to enable computer reasoning. An inference engine uses these rules to solve problems and draw conclusions. Key characteristics: • Explicit knowledge representation based on rules and logic (e.g., if-then format) • Transparent reasoning process, offering high explainability • Structural similarity to human logical thinking • Applied in expert systems, planning, knowledge representation, and semantic networks For example, in domains that require clear rule-based judgment — such as medical diagnosis systems or fault-finding support tools in manufacturing — symbolic AI can achieve high reliability. However, the costs of building and updating rule sets, and its difficulty handling ambiguity and exceptions, have led to machine learning (especially deep learning) becoming the dominant approach today. That said, combining symbolic AI with statistical AI (sub-symbolic AI) in the form of "hybrid AI" or "neuro-symbolic AI" is now attracting attention as a way to restore explainability and logical reasoning. Symbolic AI is being re-evaluated in this light. In short, symbolic AI is an approach to making computers act intelligently by explicitly teaching them knowledge as rules — it can be seen as the origin of AI itself.