How forward and backward chaining work?

Forward chaining and backward chaining are two inference methods used in Artificial Intelligence and logic programming. Forward chaining starts with known facts and applies rules to derive new facts until a goal is reached, making it data-driven. In contrast, backward chaining begins with a goal and works backward to determine which facts or rules must be true to support that goal, making it goal-driven. Both methods are useful in different scenariOS, with forward chaining being effective for automated reasoning and backward chaining often used in theorem proving and problem-solving.