Forward chaining
From Free net encyclopedia
Forward chaining is one of the two main methods of reasoning when using inference rules (in artificial intelligence). The other is backward chaining.
Forward chaining starts with the available data and uses inference rules to extract more data (from an end user for example) until an optimal goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the If clause is known to be true. When found it can conclude, or infer, the Then clause, resulting in the addition of new information to its dataset.
Inference engines will often cycle through this process until an optimal goal is reached.
For example:
I have a pet named Fritz, he's green and he hops, what is he?
- If Fritz hops - Then Fritz is green
- If Fritz is green - Then Fritz is a frog
Forward-chaining inference is often called data driven — in contrast to backward-chaining inference, which is referred to as goal driven reasoning. The top-down approach of forward chaining is commonly used in expert systems, such as CLIPS.de:Vorwärtsverkettung ja:前向き連鎖 ko:전방 추론 pl:Wnioskowanie w przód