Backward Chaining

Backward Chaining

Backward Chaining is an inference strategy employed in artificial intelligence and knowledge-based systems, wherein the reasoning process proceeds from the desired goal towards the available facts and data. It aims to determine the conditions that are necessary to reach the desired outcome by backwardly traversing through a set of logical rules and facts.

Working Principle

The backward chaining process starts with a given goal or hypothesis and then recursively tries to match the goal with the consequent of a rule in the knowledge base. If a match is found, the process continues by recursively trying to satisfy the antecedents of that particular rule. This process continues until either the initial goal is proven or it is determined that a set of conditions required for the goal to be satisfied cannot be fulfilled.

Steps Involved

Set the goal: Define the desired outcome or hypothesis to be proven.
Retrieve rules: Fetch a set of rules from the knowledge base that contain the desired outcome as the consequent.
Check antecedents: For each rule, verify if the antecedents or conditions of that rule are satisfied.
Recursively repeat: If the antecedents of a rule are satisfied, recursively repeat steps 2 and 3 by setting the goal to be the antecedents of the matching rule.
Prove or backtrack: Continue the process until the goal is proven or it is determined that the conditions required for proving the goal cannot be fulfilled.

Advantages

Efficiency: Backward chaining focuses on reaching a specific goal, which can be efficient when dealing with large knowledge bases.
Ability to handle uncertainty: Backward chaining allows for the incorporation of uncertain or incomplete information by using available rules and facts.
Flexible reasoning: It enables the system to combine both declarative knowledge (rules) and procedural knowledge (inference process).