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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.
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 ...
Expert systems require knowledge engineers to input the data, data acquisition is very hard. The expert system may choose the most inappropriate method for solving a particular problem. Problems of ethics in the use of any form of AI are very relevant at present.
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.
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]
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.
Reason maintenance [1] [2] is a knowledge representation approach to efficient handling of inferred information that is explicitly stored. Reason maintenance distinguishes between base facts, which can be defeated, and derived facts.
A knowledge as a service provider responds to knowledge requests from users through a centralised knowledge server, and provides an interface between users and data owners. [2] [3] KaaS is one of several cloud computing-dependent business models in which computer resources are sold on an on-demand and pay-as-you-use basis. [4]