What Is Backward Chaining?

Backward chaining is something that works very quickly and effectively. Just a great boon to your organization. It is a method of inference that works backwards from a goal. It's sometimes called backward reasoning because you're working backwards from the conclusion. You probably use this method every day, day in day out, without realizing it: when you try to solve a problem, you start with the final solution, then work your way back through the steps by trying different things until you find one that works. The same happens in artificial intelligence and automated theorem provers, where they try to find what pattern could fit into their program to solve a problem. The method of reasoning known as "backward chaining" starts with a set of objectives and works backward to assess whether or not there is any evidence to support those objectives. It's one of the most commonly used methods of reasoning with interference rules and logical implications. An inference engine that uses backward chaining searches the inference rules until it finds a rule with a consequent that matches the desired goal, then adds this rule to the list of plans so that data can be found to confirm it. If the antecedent of this rule is not known to be accurate, we search for more practices like it until we find one whose consequent is already known. Backward chaining is used in logic programming through selective linear definite clause resolution (SLDCLR), another method for reasoning with interference rules and logical implications. SLDCLR works by adding new clauses to our knowledge base as needed while searching for data to support them; once we've found some supporting evidence, we remove those clauses from our search, so they don't get added again unnecessarily later on down the line!