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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 and reasoning (KRR, KR&R, or KR²) is a field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog.
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.
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.
Cognitive computing refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing.These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision (object recognition), human–computer interaction, dialog and narrative generation, among other technologies.
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]
Another possibility is to integrate Fuzzy Rule Interpolation (FRI) and use sparse fuzzy rule-bases [13] instead of discrete Q-tables or ANNs, which has the advantage of being a human-readable knowledge representation form. Function approximation may speed up learning in finite problems, due to the fact that the algorithm can generalize earlier ...
In this paper, and many that came after, the formal mathematical problem was a starting point for more general discussions of the difficulty of knowledge representation for artificial intelligence. Issues such as how to provide rational default assumptions and what humans consider common sense in a virtual environment.