forwardchaining

Forward chaining, also known as data-driven inference, is a rule-based reasoning technique used in artificial intelligence and expert systems. It starts from the knowledge base’s known facts and iteratively applies production rules to derive new facts until no more rules can fire or a goal is reached.

In a forward-chaining system, the knowledge base consists of a set of facts stored in working memory and a rule base containing if-then rules. An inference engine monitors the working memory and selects applicable rules where the antecedent matches current facts. When a rule fires, its consequent is asserted as a new fact, potentially triggering additional rules. This data-driven process continues until a query is entailed or no further rules apply.

Forward chaining is well suited to problems where data arrives incrementally, where many facts are initially known, or where a broad set of implications must be explored. It is commonly used in production systems, expert systems, medical diagnosis, and business rule processing. Classic implementations include OPS5, CLIPS, and Jess; the Rete algorithm is a common efficiency technique for pattern matching in such systems.

Limitations include the possible generation of many intermediate facts, high memory usage, and computational expense in large rule bases. It may also produce results not directly relevant to a specific goal. In such cases, backward chaining or hybrid approaches are used, blending data-driven and goal-driven reasoning. Backward chaining is goal-driven, starting from a query and attempting to prove it by working backward to supporting facts.