Knowledge Representation

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. Knowledge-based techniques have been applied successfully for many computational tasks including text interpretation and cognitive robotics. Many different general architectures have been used for knowledge representation, including first-order logic, other formal logics, semantic networks, and frame-based systems. The representation of temporal knowledge is both a problem of central importance in knowledge representation and an archetype of the kinds of issues that arise in developing representations for various domains. The use of machine learning techniques for the automatic construction of knowledge bases and knowledge representations is difficult, but has achieved some degree of success.