Symbolic AI, also known as Good Old-Fashioned AI (GOFAI), is an approach to artificial intelligence that represents knowledge using symbols and applies logic-based reasoning to solve problems.

Key Concepts of Symbolic AI

1. Symbols and Rules Information is represented as symbols (e.g., "Cat", "Mammal", "Has Tail"). Logic-based rules define relationships between symbols (e.g., "If X is a cat, then X is a mammal").
2. Knowledge Representation Uses ontologies, semantic networks, and knowledge graphs to store structured information. Example: "Socrates is a man" + "All men are mortal" → "Socrates is mortal".
3. Reasoning and Inference Uses logical reasoning (e.g., deductive reasoning) to derive conclusions. Common techniques: Propositional logic (true/false statements) Predicate logic (relations between entities) Rule-based systems (IF-THEN rules)
4. Expert Systems Symbolic AI is used in expert systems, which mimic human decision-making in domains like medicine and finance. Example: MYCIN (an AI system for diagnosing bacterial infections).
5. Planning and Problem Solving AI uses search algorithms (e.g., A*, depth-first search) to explore possible solutions. Used in robotics and game AI. Examples of Symbolic AI Knowledge Graphs (e.g., Google Knowledge Graph) Expert Systems (e.g., IBM Watson) Rule-Based Chatbots (early AI chatbots before deep learning) Automated Theorem Provers (e.g., Prolog-based systems)

Strengths of Symbolic AI
✅ Interpretable & Explainable – Human-readable logic and rules.
✅ Good for Structured Knowledge – Works well in legal, medical, and financial domains.
✅ Logical Reasoning – Can perform complex reasoning without large datasets.

Weaknesses of Symbolic AI
❌ Lack of Learning Ability – Cannot automatically learn from data (unlike machine learning).
❌ Hard to Scale – Requires manual rule creation, making it difficult for complex, real-world problems.
❌ Struggles with Uncertainty – Not good at handling ambiguous or probabilistic situations.

Symbolic AI vs. Machine Learning

Feature	Symbolic AI	Machine Learning
Knowledge Representation	Symbol-based	Data-driven
Learning Ability	No learning, rule-based	Learns from data
Interpretability	Highly explainable	Often a "black box"
Adaptability	Rigid, requires manual updates	Adapts and generalizes

Modern AI often combines Symbolic AI + Machine Learning (Hybrid AI) to leverage the strengths of both.