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[6] Expert systems gave us the terminology still in use today where AI systems are divided into a knowledge base, which includes facts and rules about a problem domain, and an inference engine, which applies the knowledge in the knowledge base to answer questions and solve problems in the domain. In these early systems the facts in the ...
These issues led to the second approach to knowledge engineering: the development of custom methodologies specifically designed to build expert systems. [1] One of the first and most popular of such methodologies custom designed for expert systems was the Knowledge Acquisition and Documentation Structuring (KADS) methodology developed in Europe.
Its core is written in Java using XML and/or Office-based formats for the knowledge storage. All of its components are distributed under the terms of the Lesser General Public Licence ( LGPL ). The d3web diagnostic core implements reasoning and persistence components for problem-solving knowledge including decision trees , (heuristic) rules ...
In artificial intelligence (AI), an expert system is a computer system emulating the decision-making ability of a human expert. [1] Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural programming code. [2]
It encompasses a variety of techniques that share a common architecture: [3] two components—a reasoner and a reason maintenance system—communicate with each other via an interface. The reasoner uses the reason maintenance system to record its inferences and justifications of ("reasons" for) the inferences.
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 ...
DLs are used in artificial intelligence to describe and reason about the relevant concepts of an application domain (known as terminological knowledge). It is of particular importance in providing a logical formalism for ontologies and the Semantic Web : the Web Ontology Language (OWL) and its profiles are based on DLs.
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.