Ron Brachman has been doing influential work in knowledge representation since the time

of his Ph.D. thesis at Harvard in 1977, the result of which was the KL-ONE system, which

initiated the entire line of research on description logics. For the majority of his career he

served in research management at AT&T, first at Bell Labs and then at AT&T Labs, where

he was Communications Services Research Vice President, and where he built one of the

premier research groups in the world in Artificial Intelligence. He is a Founding Fellow of the

American Association for Artificial Intelligence (AAAI), and also a Fellowof the Association for

Computing Machinery (ACM). He is currently President of the AAAI. He served as Secretary-

Treasurer of the International Joint Conferences on Artificial Intelligence (IJCAI) for nine

years. With more than 60 technical publications in knowledge representation and related

areas to his credit, he has led a number of important knowledge representation systems efforts,

including the CLASSIC project at AT&T,which resulted ina commercially deployed systemthat

processedmore than $5 billion worth of equipment orders. Brachman is currently Director of

the Information Processing TechnologyOffice at theU.S.Defense AdvancedResearch Projects

Agency (DARPA), where he is leading a new national-scale initiative in cognitive systems.

Hector Levesque has been teaching knowledge representation and reasoning at the Univer-

sity of Toronto since joining the faculty there in 1984. He has published over 60 research

papers in the area, including three that have won best-paper awards. He has also co-authored

a book on the logic of knowledge bases and the widely used TELL–ASK interface that he

pioneered in his Ph.D. thesis. He and his collaborators have initiated important new lines of

research on a number of topics, including implicit and explicit belief, vivid reasoning, new

methods for satisfiability, and cognitive robotics. In 1985, he became the first non-American

to receive the Computers and Thought Award given by IJCAI. He was the recipient of an

E.W.R. Steacie Memorial Fellowship from the Natural Sciences and Engineering Research

Council of Canada for 1990–1991. Hewas also a Fellowof the Canadian Institute for Advanced

Research from 1984 to 1995, and is a Founding Fellow of the AAAI. He was elected to the

Executive Council of the AAAI, and is on the editorial board of five journals. In 2001, Levesque

was the Conference Chair of the IJCAI-01 conference, and is currently Past President of the

IJCAI Board of Trustees.

Brachman and Levesque have beenworking together on knowledge representation and rea-

soning for more than 25 years. In their early collaborations at BBN and Schlumberger, they

produced widely read work on key issues in the field, as well as several well-known knowledge

representation systems, including KL-ONE, KRYPTON, and KANDOR. They presented a tutorial

on knowledge representation at the International Joint Conference on Artificial Intelligence in

1983. In 1984, they coauthored a prize-winning paper at the National Conference on Artificial

Intelligence that is generally regarded as the impetus for an explosion of work in description

logics and which inspired many new research efforts on the tractability of knowledge rep-

resentation systems, including hundreds of research papers. The following year, they edited

a popular collection, Readings in Knowledge Representation, the first text in the area. With

Ray Reiter, they founded and chaired the international conferences on Principles of Knowl-

edge Representation and Reasoning in 1989; these conferences continue on to this day. Since

1992, they have worked together on the course in knowledge representation at the University

of Toronto that is the basis for this book.

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