Forward chaining is a method of reasoning that is used in artificial intelligence (AI) and computer science to solve problems by starting with the available data and working forwards to deduce new conclusions. It is often used in rule-based systems, where the goal is to find a set of rules that can be applied to a given problem to arrive at a solution.

In forward chaining, the system starts by identifying the available data and then applies a set of rules to deduce new conclusions or take actions based on that data. It checks the current state of the system and determines what actions or steps can be taken based on the available data. If the available data is not sufficient to deduce a new conclusion or take an action, the system waits until more data becomes available.

Forward chaining is useful for solving problems where the steps needed to achieve a goal are known, but the goal itself is not. It allows the system to work forwards from the available data to deduce new conclusions or take actions, rather than working backwards from the goal as in backward chaining.

Forward chaining is often used in expert systems, where it can be used to identify the set of rules that need to be applied in order to solve a particular problem. It is also used in decision support systems, where it can be used to analyze and make recommendations based on a given set of data.