Encadeamento para frente é uma reasoning technique used in inteligência artificial (AI), particularly within sistemas especialistas 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 motor de inferência 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 base de conhecimento 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 encadeamento para trás, where the system starts with a goal and works backward to determine what facts are necessary to support that goal.
As aplicações do encadeamento para frente incluem sistemas especialistas em áreas como medical diagnosis, troubleshooting, and decision support systems, where the system can continuously update its conclusions as new facts are introduced.