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Forward chaining and backward chaining are two fundamental reasoning techniques used in artificial intelligence (AI) and machine learning (ML), specifically in the context of rule-based systems. Both methods are employed to make inferences or decisions based on a set of rules or knowledge base. Each has distinct mechanisms for deriving conclusions, and their applications vary depending on the problem being solved. ### Forward Chaining Forward chaining is a data-driven inference technique that starts with the available facts or known data and applies rules to derive new facts until a goal or conclusion is reached. It is often referred to as a "bottom-up" approach because it begins from the known data and progresses forward by generating new information. This technique is widely used in expert systems, decision support systems, and rule-based reasoning models where the focus is on generating all possible inferences from the available facts. #### Process of Forward Chaining: 1. Initialization: The process begins with a set of known facts or initial conditions. These facts are provided as input into the system. 2. Rule Application: The system examines the knowledge base for rules that can be triggered by the available facts. A rule is typically in the form of "IF condition THEN action." 3. Fact Generation: If the conditions of a rule are satisfied by the available facts, the rule is fired, leading to the generation of new facts. These new facts are added to the knowledge base. 4. Iteration: The process repeats itself. The __________ __________ ___ ____ ___ __________ ____ ___ ______ ____.
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