Knowledge Representation and Reasoning: (Morgan Kaufmann Series in Artificial Intelligence)

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. *Authors are well-recognized experts in the field who have applied the techniques to real-world problems * Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems *Offers the first true synthesis of the field in over a decade

Review :
"This book clearly and concisely distills decades of work in AI on representing information in an efficient and general manner. The information is valuable not only for AI researchers, but also for people working on logical databases, XML, and the semantic web: read this book, and avoid reinventing the wheel!" Henry Kautz, University of Washington "Brachman and Levesque describe better than I have seen elsewhere, the range of formalisms between full first order logic at its most expressive and formalisms that compromise expressiveness for computation speed. Theirs are the most even-handed explanations I have seen." John McCarthy, Stanford "This textbook makes teaching my KR course much easier. It provides a solid foundation and starting point for further studies. While it does not (and cannot) cover all the topics that I tackle in an advanced course on KR, it provides the basics and the background assumptions behind KR research. Together with current research literature, it is the perfect choice for a graduate KR course." Bernhard Nebel, University of Freiburg "This is a superb, clearly written, comprehensive overview of nearly all the major issues, ideas, and techniques of this important branch of artificial intelligence, written by two of the masters of the field. The examples are well chosen, and the explanations are illuminating. Thank you for giving me this opportunity to review and praise a book that has sorely been needed by the KRR community." Bill Rapaport, University at Buffalo "A concise and lucid exposition of the major topics in knowledge representation, from two of the leading authorities in the field. It provides a thorough grounding, a wide variety of useful examples and exercises, and some thought-provoking new ideas for the expert reader." Stuart Russell, UC Berkeley "Brachman and Levesque have laid much of the foundations of the field of knowledge representation and reasoning. This textbook pro