What is forward chaining?
Forward chaining in artificial intelligence is a method in which inference rules are applied to existing data to extract additional data until an end goal is achieved. In this type of chaining, the inference engine first evaluates existing facts, derivations, and conditions before deducing new information. An end goal is achieved through the manipulation of knowledge that exists in the knowledge base.
What are the benefits of forward chaining?
- It draws multiple conclusions.
- It provides a fair basis for arriving at conclusions.
- It’s flexible because it doesn’t have a limitation on the data derived from it.
What are the challenges of forward chaining?
- It may be time-consuming. It takes a lot of time to eliminate and synchronize available data.
- The explanation of facts or observations is not very clear.