Forward chaining is the logical process of inferring unknown truths from known data and moving forward using determined conditions and rules to find a solution. The opposite of forward chaining is backward chaining.
Generally, complex tasks can be reduced to multiple simpler tasks that are performed either simultaneously or in a sequence, like a chain. Chaining is an effective method for teaching complex skills or processes with multiple steps.
As a data-driven and bottom-up form of logic, forward chaining starts from known conditions and rules, then progresses towards a logical conclusion using if-then statements. It applies these conditions and rules to the problem until there are no further applicable situations or until a set limit is reached. Forward chaining searches for any available conclusions and can create an infinite number of possible conclusions.
In artificial intelligence (AI), forward chaining is used to help an AI agent solve logic problems by inspecting rules and previous learning to deduce ways to find solutions. An AI might use forward chaining to explore the available information, answer a question or solve a problem. Forward chaining is used to break down the logic sequence and work through it from beginning to end by attaching each step after the previous one is solved.
Forward chaining and its counterpart backward chaining represent deductive logic. In contrast, backward chaining moves backward from a conclusion to find the rules or conditions from which it resulted