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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 ...
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.
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.
Knowledge Acquisition and Documentation Structuring (KADS) is a structured way of developing knowledge-based systems (expert systems). It was developed at the University of Amsterdam as an alternative to an evolutionary approach and is now accepted as the European standard for knowledge based systems. [1] Its components are:
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]
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.
Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts.
knowledge-based: refers only to the system's architecture – it represents knowledge explicitly, rather than as procedural code; Today, virtually all expert systems are knowledge-based, whereas knowledge-based system architecture is used in a wide range of types of system designed for a variety of tasks.