Forward and backward chaining in Artificial intelligence PDF

Forward chaining is known as data-driven technique because we reaches to the goal using the available data. Backward chaining is known as goal-driven technique because we start from the goal and reaches the initial state in order to extract the facts. 3. It is a bottom-up approach. It is a top-down approach.

Forward chaining in artificial intelligence is a method in which inference rules are applied to existing data to extract additional data until an end goal is achieved. In this type of chaining, the inference engine first evaluates existing facts, derivations, and conditions before deducing new information.

Backward chaining is the logical process of inferring unknown truths from known conclusions by moving backward from a solution to determine the initial conditions and rules. Backward chaining is often applied in artificial intelligence (AI) and may be used along with its counterpart, forward chaining.

Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true.