Forward Chaining
Forward chaining is a reasoning method in artificial intelligence and expert systems where inference starts from known facts and applies rules to derive new facts until a goal is reached or no more rules can fire. It is a data-driven approach that moves from initial conditions toward conclusions, commonly used in rule-based systems and production systems. This technique is foundational in areas like automated planning, diagnostic systems, and business rule engines.
Developers should learn forward chaining when building systems that require automated decision-making based on evolving data, such as in real-time monitoring, fraud detection, or workflow automation. It is particularly useful in scenarios where rules need to be applied iteratively as new information becomes available, such as in expert systems for medical diagnosis or industrial control systems. Understanding forward chaining helps in designing efficient inference engines and integrating logic into applications that handle dynamic data streams.