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  2. Algorithmic inference - Wikipedia

    en.wikipedia.org/wiki/Algorithmic_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 ).

  3. Backward chaining - Wikipedia

    en.wikipedia.org/wiki/Backward_chaining

    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 ...

  4. Automated reasoning - Wikipedia

    en.wikipedia.org/wiki/Automated_reasoning

    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.

  5. Inference engine - Wikipedia

    en.wikipedia.org/wiki/Inference_engine

    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.

  6. Automated machine learning - Wikipedia

    en.wikipedia.org/wiki/Automated_machine_learning

    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.

  7. Knowledge representation and reasoning - Wikipedia

    en.wikipedia.org/wiki/Knowledge_representation...

    Also, data structures and algorithms for general fast search. In this area, there is a strong overlap with research in data structures and algorithms in computer science. In early systems, the Lisp programming language, which was modeled after the lambda calculus, was often used as a form of functional knowledge representation. Frames and Rules ...

  8. How to watch Chiefs vs. Bills today: Start time, TV channel ...

    www.aol.com/watch-chiefs-vs-bills-today...

    Week 11 of the NFL season will feature a must-watch matchup between the Kansas City Chiefs and the Buffalo Bills, a game that could potentially shape the playoff picture in the AFC.. The Chiefs ...

  9. Semantic reasoner - Wikipedia

    en.wikipedia.org/wiki/Semantic_reasoner

    DIP, Defeasible-Inference Platform (DIP) is an Web Ontology Language reasoner and Protégé desktop plugin for representing and reasoning with defeasible subsumption. [3] It implements a Preferential entailment style of reasoning that reduces to "classical entailment" i.e., without the need to modify the underlying decision procedure.