Backward chaining is more focused than forward chaining, because it only processes rules that are relevant to the question. It is similar to how resolution is used in predicate logic.
- Backward chaining is used for advisory expert systems, where users ask questions and get asked leading questions to find an answer. Mycin is an example that used the backward chaining of bacterial infections in medical patients (Shortliffe 1976).
Backward Algorithm
1. Load the rule base into the inference engine, and any facts from the knowledge base into the working memory.
2. Add any additional initial data into the working memory.
3. Specify a goal variable for the inference engine to find.
4. Find the set of rules which refer to the goal variable in a consequent clause. That is, find all rules which set the value of the goal variable when they fire. Put each rule on the goal stack.
5. If the goal stack is empty, halt.
6. Take the top rule off the goal stack.
7. Try to prove the rule is true by testing all antecedent clauses to see if they are true. We test each antecedent clause in turn:
(A) If the clause is true, go on to the next antecedent clause.
(B) If the clause is false, then pop the rule off the goal stack; go to step 5.
(C) If the truth value is unknown because the antecedent variable is unknown, go to step 4, with the antecedent variable as the new goal variable.
المادة المعروضة اعلاه هي مدخل الى المحاضرة المرفوعة بواسطة استاذ(ة) المادة . وقد تبدو لك غير متكاملة . حيث يضع استاذ المادة في بعض الاحيان فقط الجزء الاول من المحاضرة من اجل الاطلاع على ما ستقوم بتحميله لاحقا . في نظام التعليم الالكتروني نوفر هذه الخدمة لكي نبقيك على اطلاع حول محتوى الملف الذي ستقوم بتحميله .
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