Symbolic AI
Symbolic AI, also known as classical AI or rule-based AI, is an approach to artificial intelligence that uses symbolic representations of problems, logic, and rules to perform reasoning and problem-solving. It involves manipulating symbols and applying logical rules to derive conclusions, often using techniques like knowledge representation, inference engines, and expert systems. This contrasts with subsymbolic approaches like neural networks, which learn patterns from data without explicit symbolic rules.
Developers should learn Symbolic AI when building systems that require transparent, explainable decision-making based on explicit rules, such as in legal reasoning, medical diagnosis, or formal verification. It is particularly useful in domains where logic, reasoning, and human-interpretable knowledge are critical, as it allows for precise control and debugging of AI behavior. This approach is foundational for understanding AI history and complements modern data-driven methods in hybrid AI systems.