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  2. Knowledge acquisition - Wikipedia

    en.wikipedia.org/wiki/Knowledge_acquisition

    Knowledge 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 ...

  3. Ripple-down rules - Wikipedia

    en.wikipedia.org/wiki/Ripple-down_rules

    Ripple-down rules consist of a data structure and knowledge acquisition scenarios. Human experts' knowledge is stored in the data structure. The knowledge is coded as a set of rules. The process of transferring human experts' knowledge to Knowledge-based systems in RDR is explained in knowledge acquisition scenario.

  4. Knowledge representation and reasoning - Wikipedia

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

    Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.

  5. Expert system - Wikipedia

    en.wikipedia.org/wiki/Expert_system

    Expert systems were the first commercial systems to use a knowledge-based architecture. In general view, an expert system includes the following components: a knowledge base, an inference engine, an explanation facility, a knowledge acquisition facility, and a user interface. [48] [49] The knowledge base represents facts about the world.

  6. IA-32 - Wikipedia

    en.wikipedia.org/wiki/IA-32

    The primary defining characteristic of IA-32 is the availability of 32-bit general-purpose processor registers (for example, EAX and EBX), 32-bit integer arithmetic and logical operations, 32-bit offsets within a segment in protected mode, and the translation of segmented addresses to 32-bit linear addresses. The designers took the opportunity ...

  7. Inductive logic programming - Wikipedia

    en.wikipedia.org/wiki/Inductive_logic_programming

    Inductive logic programming has adopted several different learning settings, the most common of which are learning from entailment and learning from interpretations. [16] In both cases, the input is provided in the form of background knowledge B, a logical theory (commonly in the form of clauses used in logic programming), as well as positive and negative examples, denoted + and respectively.

  8. 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.

  9. Artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence

    Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]