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Backward chaining

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TL;DR. An inference method that starts from a potential outcome and works backward to find supporting evidence or conditions.

Technical Definition

An inference method that starts from a potential outcome and works backward to find supporting evidence or conditions.

How it works

Backward chaining is a reasoning strategy used in artificial intelligence, particularly in expert systems and theorem provers. It begins with a goal or hypothesis and systematically searches for rules and facts that would support it, working backward from the conclusion to the premises.

Related Concepts

  • Expert system — AI programs that mimic the decision-making ability of a human expert using a knowledge base and inference rules.
  • Inference engine — A component of an AI system that uses logical rules to deduce new information from a knowledge base.
  • Rule-based system — AI systems that use human-defined rules to store and manipulate knowledge.

Further Reading

  • Wikipedia — Glossary of AI