Forward chaining
Posted by amuhb on November 18, 2009
Forward chaining is one of the two main methods of reasoning when using inference rules (in artificial intelligence).
suppose that the goal is to conclude the color of a pet named Fritz, given that he croaks and eats flies, and that the rule base contains the following four rules:
- If X croaks and eats flies – Then X is a frog
- If X chirps and sings – Then X is a canary
- If X is a frog – Then X is green
- If X is a canary – Then X is yellow
This rule base would be searched and the first rule would be selected, because its antecedent (If Fritz croaks and eats flies) matches our data. Now the consequents (Then X is a frog) is added to the data. The rule base is again searched and this time the third rule is selected, because its antecedent (If Fritz is a frog) matches our data that was just confirmed. Now the new consequent (Then Fritz is green) is added to our data. Nothing more can be inferred from this information, but we have now accomplished our goal of determining the color of Fritz.
The opposite of forward chaining is backward chaining.
View full Wikipedia article.
Backward chaining « AMuHb Lab said
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