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Forward Chaining

/ˈfɔː.wəd ˈtʃeɪ.nɪŋ/noun
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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.

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