Forward Chaining
An inference engine is a component of an intelligent system in artificial intelligence that applies logical rules to a knowledge base to infer new information from known facts. An inference engine typically operates in two modes:
- Forward Chaining
- Backward Chaining
Forward Chaining is a method of reasoning. When using an inference engine, forward chaining is also called forward deduction or forward reasoning method, which is used to derive conclusions step by step based on known facts.In a rule-based reasoning system, it starts with a known starting fact or rule, and gradually derives new conclusions by matching the conditional part of the rule and performing corresponding actions based on the matching results. This process continues until the desired goal is achieved or no further deduction is possible. Forward chaining is mainly used in the field of expert systems and artificial intelligence for inference and decision making.
Forward chaining starts with an atomic statement in the knowledge base and applies inference rules (Modus Ponens) in a forward direction to extract more data until the goal is reached. The forward chaining algorithm starts with the known facts, triggers all rules that satisfy their premises, and adds their conclusions to the known facts. This process is repeated until the problem is solved.
Properties of forward chaining:
- It is a bottom-up approach, which moves from the bottom to the top.
- It is the process of drawing conclusions based on known facts or data by starting from an initial state and reaching a target state.
- When we use available data to reach our goal, the forward chaining method is also called data-driven.
- The forward chaining method is often used in expert systems such as CLIPS, business and production rule systems.