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The inference algorithm gets the sample and computes a representation consistent with the sample. The goal is that when the inference algorithm receives a characteristic sample for a language , or a sample that subsumes a characteristic sample for , it will return a representation that recognizes exactly the language .
Jena, an open-source semantic-web framework for Java which includes a number of different semantic-reasoning modules. OWLSharp, a lightweight and friendly .NET library for realizing intelligent Semantic Web applications. NRules a forward-chaining inference-based rules engine implemented in C# which uses an enhanced implementation of the Rete ...
Probabilistic programming (PP) is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. [1] It represents an attempt to unify probabilistic modeling and traditional general purpose programming in order to make the former easier and more widely applicable.
Because the list of goals determines which rules are selected and used, this method is called goal-driven, in contrast to data-driven forward-chaining inference. The backward chaining approach is often employed by expert systems. Programming languages such as Prolog, Knowledge Machine and ECLiPSe support backward chaining within their inference ...
Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field are computational learning theory , granular computing , bioinformatics , and, long ago, structural probability ( Fraser 1966 ).
Forward chaining (or forward reasoning) is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems , business and production rule systems .
John Pollock's OSCAR system [2] is an example of an automated argumentation system that is more specific than being just an automated theorem prover. Tools and techniques of automated reasoning include the classical logics and calculi, fuzzy logic, Bayesian inference, reasoning with maximal entropy and many less formal ad hoc techniques.
In the field of artificial intelligence, an inference engine is a software component of an intelligent system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine.