Difference between Backward Chaining and Forward Chaining

Forward Chaining

Artificial intelligence (AI) and expert systems employ the reasoning technique of "forward chaining" to draw inferences or achieve objectives based on information and rules. It is a data-driven methodology that begins with known facts and repeatedly applies rules to produce new derived facts until no more rules are applicable or until a specific objective is attained.

The forward chaining procedure may be summed up as follows:

• Initialization: The system is set up with the initial facts or data provided.
• Rule Application: Based on the conditions and actions of the rule, it applies relevant rules to the existing facts and produces new derived facts.
• Fact Integration: The recently discovered facts are added to the already-existing facts.
• Iteration: The procedure is repeated, and the system keeps using the updated set of facts to apply the rules, perhaps producing new derived facts.
• Goal Attainment: The system updates facts and applies rules until the goal is achieved or no more rules can be applied.

Advantages

1. Incremental and Reactive: It modifies findings in response to new information as it becomes available.

2. Effective use of data: It avoids computations that aren't essential by only activating rules when the required data is available.

3. Conducive to real-time systems: a good fit for systems that must react fast to shifting circumstances.

Backward Chaining

In artificial intelligence and expert systems, backward chaining is a technique for finding the facts or body of evidence that best supports a specific objective or conclusion. It is a goal-driven methodology that begins with a desired goal and works backward through the rules and information to uncover the requisite proof or conditions to fulfil the goal.

The backward chaining procedure may be summed up as follows:

• Initialization of the objective: The system begins with a particular objective that must be accomplished or proven.
• Rule evaluation: It looks at the rules in reverse order to see which would help achieve the aim.
• Fact Check: The system verifies the relevant facts needed to satisfy the chosen rule(s).
• Sub-Goal Generation: Without the required facts, the system considers the rule's conclusion a brand-new sub-goal and recursively employs backward chaining to achieve the sub-goal.
• Iteration: The procedure iterates until all sub-objectives are met or until no further conclusions can be traced back, whichever comes first.
• Aim Attainment: When all pertinent information is at hand, the initial aim is accomplished, and the goal's fulfilment is demonstrated.

Advantages

1. Goal-oriented: It starts with the end in mind and works backward to identify the information or guidelines required to get there.

2. Reduced search area: It concentrates solely on the rules and information pertinent to achieving the objective, resulting in a more effective search.

3. Good for fixing problems: Effective in activities that demand the system to solve problems and decide the procedures necessary to reach a particular objective.

Differences