Difference between Backward and Forward Chaining.
Forward Chaining and Backward Chaining are the two most important strategies in the field of Artificial Intelligence and lie in the Expert System Domain of AI.
Forward and Backward chaining is the strategies used by the Inference Engine in making the deductions.
Inference Engine:
Inference Engine is a component of the expert system that applies logical rules to the knowledge base to deduce new information. It interprets and evaluates the facts in the knowledge base in order to provide an answer.
A knowledgebase is a structured collection of facts about the system’s domain.
Forward Chaining:
Forward Chaining the Inference Engine goes through all the facts, conditions and derivations before deducing the outcome i.e When based on available data a decision is taken then the process is called as Forwarding chaining, It works from an initial state and reaches to the goal(final decision).
Example:
A
A -> B
B
—————————–
He is running.
If he is running, he sweats.
He is sweating.
Backward Chaining:
In this, the inference system knows the final decision or goal, this system starts from the goal and works backwards to determine what facts must be asserted so that the goal can be achieved, i.e it works from goal(final decision) and reaches the initial state.
Example:
B
A -> B
A
—————————–
He is sweating.
If he is running, he sweats.
He is running.
Difference between Forwarding Chaining and Backward Chaining:
Forward Chaining | Backward Chaining
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