Explain Forward Chaining Systems and Backward Chaining Systems with a suitable example for each.

Forward Chaining and Backward Chaining are two fundamental inference techniques used in artificial intelligence (AI) and machine learning systems, particularly in the context of rule-based expert systems. These systems rely on a knowledge base consisting of rules, facts, and logic to derive conclusions or make decisions. Forward chaining and backward chaining are different strategies employed to deduce information, depending on the flow of reasoning.

Forward Chaining Systems

Forward chaining, also known as data-driven reasoning, is a method that begins with available facts and applies inference rules to derive new facts or conclusions until a goal is reached. The process starts with the known facts and proceeds forward to gather more information, building upon the existing knowledge incrementally.

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