Forward Chaining ist ein reasoning technique used in künstliche Intelligenz (AI), particularly within Expertensysteme and rule-based systems. It operates on a set of known facts and a collection of rules to derive new information or conclusions. In this approach, the Inferenz-Engine starts with the available data and applies the rules to infer new facts until a goal is reached or no more rules can be applied.
The process begins with an initial set of known facts. The system systematically examines the rules to identify any that are applicable to the current set of facts. When a rule’s conditions (also known as its antecedents) are satisfied, the rule is triggered, and its conclusions (or consequents) are added to the Wissensbasis as new facts. This process continues iteratively, expanding the knowledge base until the system reaches a predetermined goal or exhausts all possible rules.
Forward chaining is particularly useful in scenarios where the goal is not known in advance, allowing the system to explore various paths of reasoning based on the available data. It contrasts with Backward Chaining, where the system starts with a goal and works backward to determine what facts are necessary to support that goal.
Anwendungen des Forward Chaining umfassen Expertensysteme in Bereichen wie medical diagnosis, troubleshooting, and decision support systems, where the system can continuously update its conclusions as new facts are introduced.