Forward Chaining

Forward chaining is a data-driven inference technique in artificial intelligence and logic programming, where the system begins with a set of known facts and applies rules sequentially to generate new conclusions until a goal is reached or no further inferences are possible. This method contrasts with goal-driven approaches by emphasizing breadth-first exploration, making it ideal for scenarios with uncertain or evolving data, such as real-time expert systems.