Forward Chaining

Forward chaining starts with the available Data and uses Inference Rule s 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 System s, such as CLIPS .

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Forward Chaining

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