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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
- Sales Rank: #886577 in Books
- Brand: Brand: Morgan Kaufmann
- Published on: 2004-06-02
- Original language: English
- Number of items: 1
- Dimensions: 9.50" h x 1.11" w x 7.70" l, 2.28 pounds
- Binding: Hardcover
- 381 pages
- Used Book in Good Condition
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 provides a lucid and comprehensive introduction to the field. It is written with the same clarity and gift for exposition as their many research publications. The text will become an invaluable resource for students and researchers alike."
Bart Selman, Cornell University
"KR&R is known as "core AI" for a reason -- it embodies some of the most basic conceptualizations and technical approaches in the field. And no researchers are more qualified to provide an in-depth introduction to the area than Brachman and Levesque, who have been at the forefront of KR&R for two decades. The book is clearly written, and is intelligently comprehensive. This is the definitive book on KR&R, and it is long overdue."
Yoav Shoham, Stanford University
From the Back Cover
A concise and lucid exposition of the major topics in knowledge representation, from two of the leading authorities in the field. -Stuart Russell, UC Berkeley
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 have been at the forefront of KR&R for two decades. This is the definitive book on KR&R, and it is long overdue. -Yoav Shoham, Stanford University
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.
About the Author
By Dr. Ronald Brachman and Hector Levesque
Most helpful customer reviews
27 of 28 people found the following review helpful.
Ok, but not enlightening
By Un francais en angleterre
I own an old edition of the classic Russell and Norvig (R&N) which I read 10 years ago and did not feel like going through the huge new 2009 edition to learn about current topics, so I went looking for something a bit more recent with a focus on knowledge representation, and came up with this book. I have to say unfortunately that while not a bad book, it does not cover much more than the old R&N (side note on this: R&N is very comprehensive and covers the full AI spectrum. This book seems biased toward one particular school of AI. This may or may not be bad for you: if you're not interested in the additional material in R&N, such as neural nets, you're possibly better off with this book. I doubt there are many of you in this case though) and tends to be less pedagogical. It is also more uneven regarding the depth at which topics are covered, with a fairly strong bias toward the topics where the authors appear to be active researcher. Such a bias would be ok for a more advanced textbook, but we're talking about a fairly introductory text here, and it feels a bit unbalanced. I cannot therefore recommend it highly, but I am not highly critical either, as I still managed to learn a couple of things. Below are detailed notes, which I hope might be of interest to outline the stronger points. As a side note, this is a very theoretical book, with no direct programming application or exercises. This did not bother may, but may not be clear from the other reviews.
The introduction sets the scene well and provides a useful conceptual background. How the following chapters are articulated against the principles discussed in the introduction is not always straightforwardly clear though. In that sense, the authors may fall a bit short of their overall goal.
The second chapter (the language of first order logic) is unlikely to be big news for anybody schooled in undergraduate mathematics, but I understand the material must be included for the sake of completeness and autonomy. The third chapter is entitled "expressing knowledge" and in my view does not really do justice to the topic, as demonstrated by the matter covered in the afterthought section "other sorts of facts": these "other facts" include statistical and probabilistic facts, default and prototypical facts, intentional facts (beliefs etc...). The book deals with some of these later to be fair.
At this point in the book, all that has been achieved is to show how one can use first order logic (FOL) to deal logically with some problems that a six years old can probably solve without the need for the framework. Chapter 4 shows that it is possible to teach FOL to a computer and to have him assess the truth of a statement formulated in FOL given a number of others FOL statements. The algorithm is not completely trivial but not overly complex either. Unfortunately, the time taken to deal with such tasks is potentially very large for problems not amazingly complex if one allows FOL statements of arbitrary structured. Chapter 5 is dedicated to the exploration of Horn clauses, which are basically a type of FOL statement for which algorithms are available that converge faster. This motivates the need to embed some hints on how to reason with a given problem within computer languages. Chapters 6 and 7 explore this respectively in the context of PROLOG and of the so-called "production rules systems". As one gets familiar with the above approaches, a number of limitations become clear and the subsequent chapters are about moving away somewhat from FOL. Chapter 8 introduces object oriented representation, using a formalism a bit on the heavy side for a concept that's actually fairly clear. Give or take a few examples, a reader of R&N is on familiar ground up to this point in the book. The next chapters, respectively on description logics and inheritance cover material that was less familiar to me and might be a reason to dig into this book. It shows a couple of neat ideas (taxonomies, inheritance networks) and how reasoning with such data structures can be difficult when one encounters contradictions. This motivates the need to clarify the concept of "default", which is done in chapter 11, another good chapter in my view. Chapter 12 includes an introduction to probabilities that probably ranks with chapter 2 as something most readers don't really need. It also covers fairly superficially bayesian networks, influence diagrams and the Dempster Shafer theory. In all honesty given the brisk pace at which this is all done, I don't think it's really possible to get much out of what's covered here.
The concepts in the next chapter (Explanation and Diagnostic) were newer to me. While not straigthforward to implement, it seems the core approach of the authors is here at an advantage over other more opaque techniques.
The next two chapters ("Actions" and "Planning") deal with topics that are closer to the preoccupation of standard AI. As they're both good topics to motivate the AI endeavour, introducing them earlier might have made more sense.
The last chapter is about "the tradeoff between expressiveness and tractability". The authors look back at the big picture that had been evoked during the introduction, but which had to some extent taken the back seat during most of the time. Fairly uncontroversially, they point out that being able to deal with very expressive languages is desirable, but typically fraught with tractability issues. One senses that the
PS: I bought the Kindle edition of the book, and as unfortunately too frequent, it suffers from some navigational issues: the table of content does not link correctly to the materials referenced (links are off by a few pages) and citations are not hyperlinked, which makes it less than user-friendly to determine what book or article stands behind the reference [137]. This is something the publishers really ought to sort out, as I cannot think of any good justification for such sloppiness.
28 of 28 people found the following review helpful.
Don't have to be a math buff to understand
By Scott P. Stewart
I came across this book looking for a text that would explain the context of First Order Logic, why it is used for so many knowledge representation problems, how it is used to solve them, and its limitations. I must say that this is far and away the best book I've found to answer these questions. If you search around a little at the competition, you will find much of the text quickly turning to mathematical proofs and deductions in their explanations. While this is of course necessary and helpful, it doesn't (for me) really give an idea of how and why these methods are used practically. You can tell that these authors spent some time on ensuring consistency and fluency of the writing, which I find so very helpful.
I'm trying to think of something bad to say about it: I wish it were longer! If you read the preface you will see the authors call it an introduction, which is definitely true. Maybe they will team up again for a more in-depth text on some aspect of this subject.
1 of 1 people found the following review helpful.
Kindle version with lot of typos and Amazon is ignoring the reports
By Alexandre Rademaker
The book is excellent but the Kindle version has a lot of typos. Actually, I found it hard to report typos to Amazon and none of the typos that I reported were fixed. That is very disappointed. I was expecting more interest from Amazon to improve the quality of the electronic books.
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