A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine.
The first part, the knowledge base, represents facts about the world, often in some form of subsumption ontology (rather than implicitly embedded in procedural code, in the way a conventional computer program does). Other common approaches in addition to a subsumption ontology include frames, conceptual graphs, and logical assertions.
The second part, the inference engine, allows new knowledge to be inferred. Most commonly, it can take the form of IF-THEN rules coupled with forward chaining or backward chaining approaches. Other approaches include the use of automated theorem provers, logic programming, blackboard systems, and term rewriting systems such as CHR (Constraint Handling Rules). These more formal approaches are covered in detail in the Wikipedia article on knowledge representation and reasoning.
Knowledge-based systems were first developed by artificial intelligence researchers. These early knowledge-based systems were primarily expert systems – in fact, the term is often used interchangeably with expert systems, although there is a difference. The difference is in the view taken to describe the system:
"expert system" refers to the type of task the system is trying to assist with – to replace or aid a human expert in a complex task typically viewed as requiring expert knowledge
"knowledge-based system" refers to the architecture of the system – that it represents knowledge explicitly, rather than as procedural code.
While the earliest knowledge-based systems were almost all expert systems, the same tools and architectures can and have since been used for a whole host of other types of systems.
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This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems.
Introduction aux techniques de l'Intelligence Artificielle, complémentée par des exercices de programmation qui montrent les algorithmes et des exemples de leur application à des problèmes pratiques.
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In 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.
L'ingénierie des connaissances est une des dimensions de la gestion des connaissances au sein d'une organisation. Elle fait référence à l'ingénierie de systèmes complexes « intelligents » incorporant beaucoup de connaissances tels les systèmes experts. L'exploitation des connaissances passe par cinq opérations : identification, création, stockage, partage et utilisation. L'ingénierie des connaissances se concentre sur l'identification, la création, le stockage et la mise à disposition des connaissances afin de rester neutre face aux outils de partage et d'utilisation.
Le terme de cadres (en anglais frames) a été proposé par Marvin Minsky dans son article de 1974 intitulé A Framework for Representing Knowledge. Un cadre en intelligence artificielle est une structure de données utilisée pour subdiviser la connaissance en sous-structures représentant des situations stéréotypées. Les cadres sont reliés entre eux pour former une idée complète. Un cadre contient de l'information sur la manière d'utiliser le cadre, sur ce qu'on peut en attendre, et sur ce qu'on peut faire lorsque cette attente n'est pas satisfaite.
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