The basics of knowledge representation and reasoning

A widely recognized goal of artificial intelligence (AI) is the creation of artifacts that can emulate humans in their ability to reason symbolically, as exemplified in typical AI domains such as planning, natural language understanding, diagnosis, and tutoring. Currently most of this work is predicated on a belief that intelligent systems can be constructed from explicit, declarative knowledge bases, which in turn are operated on by general, formal reasoning mechanisms. This fundamental hypothesis of AI means that knowledge representation and reasoning — the study of formal ways of extracting information from symbolically represented knowledge — is of central importance to the field. In this article, we review some of the basics of this important research area and briefly survey the kinds of techniques typically used for representation in AI programs. We also consider some important current research directions.