Backward chaining is an inference method which implies going backward from a successful result to infer the chain of events, conditions, or decisions that had led to that outcome. It’s like retracing your family tree to explain why you look the way you do or exhibit the characteristics that distinguish you as a person.

Backward chaining uses deductive reasoning, a method of arriving at a conclusion after establishing premises that are assumed to be true. For example, if all persons are created equal and you are a person, then you were created equal.

Backward chaining is used in artificial intelligence applications for logic programming, reasoning, and behavior analysis. It’s part of a system that aims to teach robots how to infer and make logical conclusions.

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So-called expert systems and logic forms enable backward chaining.

What Is an Expert System?

Expert systems are intelligent computer systems or programs that predate the AI-powered machines and smart devices that we know today. An expert system consists of a user interface, an inference engine that scans available facts from a knowledge base, and a knowledge base  — a library where valuable data from experts is stored. Expert systems were built to provide answers akin to human experts in a field of study, hence the name.

What Is a Logic Form?

Logic forms are simple representations of sentences formed by putting two related concepts together to form a logical argument (usually an “if and then” statement). Every noun, verb, adjective, adverb, pronoun, preposition, and conjunction in the sentence must have a predicate which tells what the subject is or does. Word senses can be added to logic forms to clarify semantics. Logic forms are utilized in some natural language processing (NLP) techniques, such as question answering and inferencing for database and quality assurance (QA) systems.