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Forward chaining

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TL;DR. A reasoning strategy that starts with data and applies rules to reach a goal, often used in expert systems.

Technical Definition

A reasoning strategy that starts with data and applies rules to reach a goal, often used in expert systems.

How it works

Forward chaining is an inference method where the system begins with known facts and applies rules to derive new facts until a desired goal is achieved. This data-driven approach is common in expert systems and rule-based AI to make deductions.

Related Concepts

  • Backward chaining — An inference method that starts from a potential outcome and works backward to find supporting evidence or conditions.
  • Inference engine — A component of an AI system that uses logical rules to deduce new information from a knowledge base.

Further Reading

  • Wikipedia — Glossary of AI