Knowledge Representation - International Encyclopedia of the Social & Behavioral Sciences. In artificial intelligence, knowledge representation is the study of how the beliefs, intentions, and value judgments of an intelligent agent can be expressed in a transparent, symbolic notation suitable for automated reasoning. From a purely computational point of view, the major objectives to be achieved are breadth of scope, expressivity, precision, support of efficient inference, learnability, robustness, and ease of construction. The knowledge representation enterprise is related to the study of semantics in linguistics, the development of logical theories in analytic philosophy, and the study of mental representations in psychology; however, its focus, objectives, and techniques are quite different from any of these. A number of general architectures for knowledge representation are described, including first- order logic, other formal logics, semantic networks, and frame- based systems. The issues involved in formulating the content of a KR theory are illustrated through a sketch of representations of temporal knowledge. Finally, the article discusses two alternative to the symbolic representations of knowledge: neural networks and statistical analysis of large data corpora. Feature Article: Data Mining: An AI Perspective 25 1. Knowledge representation. Data mining seeks to discover interesting patterns from large volumes of. Although knowledge representation is one of the. INTRODUCING MACHINE LEARNING FROM AN AI PERSPECTIVE. This paper presents our approach of introducing Machine Learning from an AI. Hence search methods and formalisms for knowledge representation and reasoning are. It also suggests adopting a broad perspective on what's. We argue as well that the attempt to deal with representation as knowledge content alone leads to an. What is a Knowledge Representation, Memo, MIT AI. Ai knowledge representation pdf Ai knowledge representation pdf Ai knowledge representation pdf DOWNLOAD!
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