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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 ).
Grammar induction (or grammatical inference) [1] is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.
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 more practical terminology, to call an object's method is considered the same as to send a message to the object. Thus, on the one hand, objects from object-oriented programming can be considered as automata (or models of automata) whose state is the combination of private fields, and one or more methods are considered to be the step. Such ...
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.
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]
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. [45] bag-of-words model A simplifying representation used in natural language processing and information retrieval (IR).