Symbolic AI, also known as rule-based AI or knowledge-based AI, is a subfield of artificial intelligence that deals with the representation and manipulation of knowledge in a symbolic form. It involves the use of logical and mathematical symbols to represent abstract concepts, rules, and relationships, which can then be reasoned about and manipulated using automated reasoning techniques. In symbolic AI, the emphasis is on building a knowledge base of rules and relationships, which can be used to solve problems and make decisions in a variety of domains. The knowledge base is typically encoded in a formal language, such as predicate calculus or first-order logic, and is manipulated using automated reasoning techniques, such as deduction or inference. Despite its limitations in dealing with the uncertainty and complexity of real-world problems, symbolic AI has been used successfully in a variety of applications, including expert systems, natural language processing, and game playing. With the advent of machine learning and deep learning techniques, symbolic AI is increasingly being combined with statistical approaches to create more powerful and flexible AI systems.