What is backward chaining?
Backward chaining in artificial intelligence involves backtracking from the endpoint or goal to steps that led to the endpoint. This type of chaining starts from the end goal and moves backward to comprehend the steps taken to attain this goal. The backtracking process can also enable a person to establish logical steps used to find other crucial solutions. It is usually used in debugging, diagnostics, and prescription applications.
What are the benefits of backward chaining?
- The result is known, which makes it easy to deduce inferences.
- It’s a quick method of reasoning because the endpoint is available.
- In backward chaining, correct solutions can be derived effectively if pre-determined rules are met by the inference engine.
What are the challenges of backward chaining?
- The reasoning starts only if the endpoint is known.
- It doesn’t deduce multiple solutions or answers.
- Less flexibility as it only derives the data that is needed.
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