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A rule-based system is an artificial intelligence system that represents knowledge in the form of facts and rules and uses an inference mechanism to derive new information or make decisions. In Programming and AI Lab, rule-based systems are important because they demonstrate how computers can imitate a simple form of human reasoning. A rule is generally represented in the form of an IF–THEN statement. The IF part describes a condition, while the THEN part specifies the conclusion or action that follows when the condition is satisfied. For example, a rule may be written as: IF a person has fever AND cough, THEN the person may have flu. Rule-based reasoning mainly uses two important inference techniques: forward chaining and backward chaining. a) Forward chaining systems Forward chaining is a data-driven inference technique. It starts with the facts or information that are already known and applies rules to derive new facts. The process continues step by step until a desired conclusion is reached or no more rules can be applied. Thus, the reasoning moves from the known facts toward the conclusion. In forward chaining, the system examines the available facts and identifies rules whose conditions match those facts. When the conditions of a rule are satisfied, the rule is fired and its conclusion is added to the set of known facts. The newly obtained fact may then ______ _______ ___ ________ ___ ____ ________.
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