Forward chaining begins by inferring a set of rules or known data and going “forward” to achieve a goal. As such, it simplifies a complex task by dividing it into several simpler tasks that a computer may carry out either synchronously or sequentially, much like in a chain of processes. The data-driven and logical process of forward chaining is thus commonly implemented in production rule and expert systems.

In artificial intelligence (AI), forward chaining helps a program come up with a solution by analyzing known data and aligning it with predetermined parameters. An example would be when an end-user uses an app to determine what kind of insect he/she is looking at. The app begins by determining how many legs the insect has, what its color is, and so on until it gains enough inputs to come up with an answer.

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What Is the Difference between Forward and Backward Chaining?

Inference engines, which are classified into forward and backward chaining systems, are critical components of an AI’s intelligent system. They are responsible for applying logical rules to the knowledge base to gather new data from predetermined facts. But they have stark differences as outlined below.

  • Forward chaining, as the name implies, begins from “known facts” and moves forward using rules to uncover more information. It will continue to do so until it attains its goal. Backward chaining, on the other hand, starts from the target and proceeds backward to determine the set of rules that match the goal.
  • Forward chaining uses the breadth-first strategy (BFS), an algorithm that allows programs to start the search process by adhering to a level-based approach in problem-solving. The system solves the problem step by step, regardless of complexity. It doesn’t go to the next level without solving the first one. Backward chaining uses the depth-first strategy (DFS), meanwhile, which starts with the problem element that requires the most complex solution before backtracking to the one that needs the simplest answer.
  • Forward chaining is data-driven. As such, it requires several ASK questions or known facts from the user’s knowledge base. On the flip side, backward chaining is goal-driven and so only needs a few ASK questions.

Why Use Forward Chaining?

AI systems use forward chaining to allow programs to explore all available data, solve a problem, or simply answer a question. With it, an application can easily break down the logic sequence and complete it from start to finish, making it a straightforward and uncomplicated method of making inferences.

What Are the Common Uses of Forward Chaining?

Forward chaining is widely used in AI systems.

Expert Systems

Expert systems use forward chaining to enable a program to achieve something that only an expert can, such as identifying insects or giving advice about stock-buying by making inferences based on available facts. Since the process is based on a set of rules, the deduction is straightforward. For example, if an animal quacks and has two legs, then it is a duck.

Production Rule Systems

Production rule systems use the inference engine of forward chaining. They are built with a set of rules about behaviors and procedures, which serve as a basic representation of the world. Like expert systems, they also use if-then statements. So, only when a specific condition is met will production start. For example, when the water in a heater meets the condition “the water is hot,” the resulting action would be to “turn off the heater.”

While forward chaining is extremely useful, it also has inefficiencies. It is only ideal to use for problems that only have one starting point and several possible endpoints. It becomes inefficient for problems that have multiple starting points and only one endpoint.