Knowledge representation in AI involves encoding information about the world in a form that a computer system can utilize to solve complex tasks, such as diagnosing a medical condition or answering questions. It bridges human understanding and machine processing by using structures like semantic networks, frames, and ontologies.

Key approaches include symbolic representation (using logic and rules), connectionist models (like neural networks), and probabilistic models (for uncertainty). Effective knowledge representation is crucial for reasoning, learning, and decision-making in AI systems, enabling them to understand, infer, and interact with the world more intelligently.