Forward chaining a process in which events or received data are considered by an entity to intelligently adapt its behavior.

There are two main methods of reasoning when using Rule of inference: backward chaining and forward chaining.

Such functions can be used for efficient forward chaining planning, learning, and execution of actions represented simultaneously at multiple levels in an embodied agent architecture.

Forward chaining is a popular implementation strategy for expert systems, business and production rule systems.

Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached.

It shares characteristics with cognitive psychology's dissociation logic and philosophy's forward chaining.

Most rules engines used by businesses are forward chaining, which can be further divided into two classes:

It is one of the two most commonly used methods of reasoning with inference rules and logical implications - the other is forward chaining.

The PRD specification defines one such resolution strategy based on forward chaining reasoning.

This practice may be done in a forward direction (forward chaining) or a backward direction (back chaining).