The continual process of obtaining, representing, reasoning, & updating knowledge within an AI system is referred to as the "knowledge cycle" in AI.

The following stages make up this process:

· Knowledge Acquisition: Information is gathered from a variety of sources, including databases, documents, experts, and even other AI systems. The goal of this stage is to gather pertinent knowledge and convert it into an appropriate representation format.

· Knowledge Representation: The key stage of the knowledge cycle is knowledge representation, where acquired information is organized and encoded in a language that AI systems can comprehend and use. The effectiveness and efficiency of knowledge processing are greatly influenced by the representation approach chosen.

· Knowledge Reasoning: During this phase, AI systems use the knowledge that has been encoded to carry out reasoning tasks like inference, deduction, or induction. AI systems can reason to create new knowledge from existing knowledge and to make defensible decisions based on the information at hand.

· Knowledge Update: The knowledge representation has to be updated when new information becomes available or as outdated knowledge is amended or rendered invalid. This phase guarantees that AI systems are current and flexible enough to respond to changing conditions.