Forward chaining is a reasoning method used in rule-based AI systems that starts from known facts and repeatedly applies logical rules to derive new facts, moving forward until it reaches a conclusion or exhausts what can be inferred. It's a data-driven approach: the system doesn't start with a specific goal in mind, it starts with what it already knows and sees where the available rules lead.
This makes forward chaining well suited to situations where the goal isn't fixed in advance, such as monitoring systems and diagnostic tools that need to continuously process incoming data and flag whatever conclusions the current facts support. It contrasts with backward chaining, which works in the opposite direction, starting from a hypothesis and working backward to see whether the known facts support it; many expert systems use one or the other depending on whether they're built to explore open-ended implications or verify a specific outcome.
