Vorwärtsverkettung

Vorwärtsverkettung, also known as forward chaining, is a reasoning method used in artificial intelligence and expert systems. It operates by starting with known facts and applying inference rules to derive new facts until a goal is reached or no new facts can be deduced. This process is data-driven, meaning it progresses forward from the available information.

The system begins with a set of initial facts, often called the working memory. These facts are then compared against the conditions of the available inference rules. When the conditions of a rule are met by the facts in the working memory, the rule "fires," and its conclusion is added as a new fact. This new fact can then satisfy the conditions of other rules, potentially leading to further deductions. The cycle of checking rules and adding new facts continues until either a specific goal fact is proven or all applicable rules have fired and no new information can be derived.

Forward chaining is commonly employed in applications where the system needs to react to new data as it becomes available or when the number of possible conclusions is vast, making it impractical to work backward from a goal. It is effective for tasks such as monitoring, control systems, and data analysis where the focus is on discovering all consequences of a given set of inputs. The approach is characterized by its systematic exploration of the knowledge base, ensuring that all possible inferences are made based on the initial facts.