Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying
to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from
the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge
can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed.
This landmark text takes the central concepts of knowledge representation developed over the last 50 years and
illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a
simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach
gives readers a solid foundation for understanding the more advanced work found in the research literature. The
presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database
management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides
the foundation in knowledge representation and reasoning that every AI practitioner needs.
