Knowledge Representation: An AI Perspective (Tutorial Monographs in Cognitive Science)

Most researchers to date in artificial intelligence has been based on the knowledge representation hypothesis, that is, the assumption that in any artificial intelligence (AI) programme there is a separate module which represents the information that the programme has about the world. As a result, a number of so-called knowlege representation formalisms have been developed for representing this kind of information in a computer. This text discusses the most popular knowledge representation languages - logic, production rules, semantics (networked and frames), and also provides a short introduction to AI systems that combine various knowledge representation languages. The knowlege representation hypothesis has been challenged by the re-emergence of a new style of computing, variously called parallel distributed processing, connectionism, or neural networks. These approaches are discussed in a separate chapter, and the arguments in favour of and against parallel distributed processing are reviewed.