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The inference engine applied logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. Inference engines work primarily in one of two modes either special rule or facts: forward chaining and backward chaining. Forward ...
An inference engine is a computer program that tries to derive answers from a knowledge base. The Cyc inference engine performs general logical deduction. [8] It also performs inductive reasoning, statistical machine learning and symbolic machine learning, and abductive reasoning.
A semantic reasoner, reasoning engine, rules engine, or simply a reasoner, is a piece of software able to infer logical consequences from a set of asserted facts or axioms. The notion of a semantic reasoner generalizes that of an inference engine , by providing a richer set of mechanisms to work with.
A typical rule-based system has four basic components: [3] A list of rules or rule base, which is a specific type of knowledge base.; An inference engine or semantic reasoner, which infers information or takes action based on the interaction of input and the rule base.
An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true. When such a rule is found, the engine can conclude, or infer, the consequent (Then clause), resulting in the addition of new information to its data. [1]
Backward chaining (or backward reasoning) is an inference method described colloquially as working backward from the goal. It is used in automated theorem provers, inference engines, proof assistants, and other artificial intelligence applications. [1]
A separate inference engine processes rules and adds, deletes, or modifies a knowledge store. Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more limited logical representation is used, Horn Clauses. Pattern-matching, specifically unification, is used in Prolog.
The free energy principle is based on the Bayesian idea of the brain as an “inference engine.” Under the free energy principle, systems pursue paths of least surprise , or equivalently, minimize the difference between predictions based on their model of the world and their sense and associated perception .