Rückwärtsverkettung

Rückwärtsverkettung, also known as backward chaining, is a method used in artificial intelligence and expert systems for reasoning from a goal to the current state. It is a top-down approach that starts with the goal and works backward to determine the steps needed to achieve it. This contrasts with forward chaining, which starts with known facts and derives new conclusions from them.

In backward chaining, the system works through a series of if-then rules. If the goal is to be achieved, the system checks the conditions specified in the rule. If the conditions are not met, the system sets new sub-goals to be achieved, which in turn may require further sub-goals. This process continues until the system reaches a point where it can confirm that the conditions are met based on the current state of knowledge.

The primary advantage of backward chaining is its ability to focus on relevant information. By starting with the goal, the system can ignore irrelevant data and concentrate on the necessary steps. However, it can also be less efficient than forward chaining in situations where the goal is not well-defined or where there are many possible paths to achieve it.

Backward chaining is commonly used in applications such as medical diagnosis, where the goal is to identify a disease based on symptoms, and in natural language processing, where the goal is to understand the meaning of a sentence. It is also a fundamental concept in logic programming languages like Prolog, where it is used to solve problems by working backward from the goal to the initial conditions.