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Backward Chaining

/ˈbæk.wərd ˈtʃeɪ.nɪŋ/noun
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Backward chaining is a logical reasoning technique in artificial intelligence and expert systems that begins with a goal or hypothesis and works backward to find the supporting facts or rules. This approach is especially efficient for problems with many possible paths, as it only explores the branches relevant to the desired outcome, making it a staple in fields like medical diagnosis and automated planning. In modern usage, it's contrasted with forward chaining for its goal-oriented efficiency in data-scarce environments.

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