Production system (computer science)A "production system " (or "production rule system") is a computer program typically used to provide some form of artificial intelligence, which consists primarily of a set of rules about behavior but it also includes the mechanism necessary to follow those rules as the system responds to states of the world. Those rules, termed productions, are a basic representation found useful in automated planning, expert systems and action selection. Productions consist of two parts: a sensory precondition (or "IF" statement) and an action (or "THEN").
Exploration de donnéesL’exploration de données, connue aussi sous l'expression de fouille de données, forage de données, prospection de données, data mining, ou encore extraction de connaissances à partir de données, a pour objet l’extraction d'un savoir ou d'une connaissance à partir de grandes quantités de données, par des méthodes automatiques ou semi-automatiques.
Reasoning systemIn information technology a reasoning system is a software system that generates conclusions from available knowledge using logical techniques such as deduction and induction. Reasoning systems play an important role in the implementation of artificial intelligence and knowledge-based systems. By the everyday usage definition of the phrase, all computer systems are reasoning systems in that they all automate some type of logic or decision.
Fifth Generation Computer SystemsThe Fifth Generation Computer Systems (FGCS) was a 10-year initiative begun in 1982 by Japan's Ministry of International Trade and Industry (MITI) to create computers using massively parallel computing and logic programming. It aimed to create an "epoch-making computer" with supercomputer-like performance and to provide a platform for future developments in artificial intelligence. FGCS was ahead of its time, and its excessive ambitions led to commercial failure.
Knowledge acquisitionKnowledge acquisition is the process used to define the rules and ontologies required for a knowledge-based system. The phrase was first used in conjunction with expert systems to describe the initial tasks associated with developing an expert system, namely finding and interviewing domain experts and capturing their knowledge via rules, objects, and frame-based ontologies. Expert systems were one of the first successful applications of artificial intelligence technology to real world business problems.
Rule-based machine learningRule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learners that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.
GeneraGenera est un système d'exploitation et un environnement de développement propriétaire pour les machines Lisp développées par Symbolics. C'est un fork d'un système développé à l'origine pour les machines Lisp du laboratoire d'intelligence artificielle du MIT, mais que Symbolics utilisait conjointement avec LMI et Texas Instruments. La guerre déclarée au AI Lab du MIT par Symbolics reste pour Richard Stallman le symptôme de la disparition de l'esprit hacker, et suscita en lui tous les ingrédients nécessaires à la création du mouvement du logiciel libre.
Symbolicsthumb|Clavier de la machine Lisp Symbolics 3600 Symbolics (Symbolics, Inc) est une entreprise informatique américaine fondée en 1979 par Russell Noftsker dont l'objectif était de commercialiser les machines Lisp du laboratoire d'intelligence artificielle du MIT. Symbolics représente la principale raison qui motiva Richard Stallman dans son projet GNU. Stallman commença par s'attaquer directement à Symbolics en implémentant le code de leur machine Lisp dans le sien, puis en transmettant ce travail à leur concurrent.
Système intelligent flouUn système intelligent flou (SIF) est un système qui intègre (implémente) de l’expertise humaine et qui vise à automatiser (imiter) le raisonnement d’experts humains face à des systèmes complexes. Il constitue une part importante de l’intelligence artificielle et du soft computing. Un système intelligent flou se base sur la théorie logique qu'est la logique floue.
Constraint satisfactionIn artificial intelligence and operations research, constraint satisfaction is the process of finding a solution through a set of constraints that impose conditions that the variables must satisfy. A solution is therefore a set of values for the variables that satisfies all constraints—that is, a point in the feasible region. The techniques used in constraint satisfaction depend on the kind of constraints being considered.
