Comparative Study of Forward and Backward Chaining in Artificial Intelligence

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dc.contributor.authorNamarta Kapoor
dc.contributor.authorNischay Bahl
dc.date.accessioned2026-02-14T05:39:56Z
dc.date.available2026-02-14T05:39:56Z
dc.date.issued2016
dc.description.abstractAn artificial intelligence system is capable of elucidating and representing knowledge along with storing and manipulating data. Knowledge could be a collection of facts and principles build up by human. It is the refined form of information. Knowledge representation is to represent knowledge in a manner that facilitates the power to draw conclusions from knowledge. Knowledge representation is a good approach as conventional procedural code is not the best way to use for solving complex problems. Frames, Semantic Nets, Systems Architecture, Rules, and Ontology are its techniques to represent knowledge. Forward and backward chaining are the two main methods of reasoning used in an inference engine. It is a very common approach for “expert systems”, business and systems. This paper focus on the concept of knowledge representation in artificial intelligence and the elaborating the comparison of forward and backward chaining.
dc.identifier.issnISSN: 2319-7242
dc.identifier.urihttp://davjalandhar.ndl.gov.in/handle/123456789/204
dc.language.isoen
dc.publisherInternational Journal Of Engineering And Computer Science Volume 5 Issue 4 April 2016, Page No. 16239-16242
dc.titleComparative Study of Forward and Backward Chaining in Artificial Intelligence
dc.typeArticle
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