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Artificial Intelligence: A Modern Approach 1st Edition
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Top Customer Reviews
If you are reading this, you will probably want the
second edition instead. It was published Dec 20, 2002.
Every chapter has been extensively rewritten.
Significant new material has been introduced to cover
areas such as constraint satisfaction, fast propositional
inference, planning graphs, internet agents, exact
probabilistic inference, Markov Chain Monte Carlo
techniques, Kalman filters, ensemble learning methods,
statistical learning, probabilistic natural language
models, probabilistic robotics, and ethical aspects of AI.
For more information, see aima.cs.berkeley.edu
1) The textbook is awfully traditional and only mentions in passing newer trends in AI. For example, case-based reasoning (or the "Yale view of AI") is mentioned, but not covered. Because AI is a new and rapidly changing field, and because AI paradigms are usually based on a small set of ontological assumptions, I believe it would not be too difficult for students to understand new paradigms. Obviously this should be a high pedagogical priority.
2) The textbook is rather condescending, with the authors strongly imposing their viewpoints. In other words, the authors are a little too dogmatic and that is reflected in the text. For example, they sometimes go about ranking paradigms.
3) The textbook is sometimes rather ambiguous when explicating certain paradigms, and the end-of-chapter problems are very, very ambiguous. One of the justifications for unclear questions is to get people thinking, but when the theoretical explications are already ambiguous it defeats the whole purpose.
4) The philosophical sections in R&N are rather naive and superficial.
In spite of its obvious shortcomings, R&N has been tremendously useful to me, and I recommend it as a reference. The good news, I've heard, is that a new edition of R&N is coming out next year where these problems are eliminated.
1. The useful exercises at the end of each chapter. 2. The discussion of simple reflex and goal-based agents. 3. The treatment of constraint satisfaction problems and heuristics for these kinds of problems. 4. The overview of iterative improvement algorithms, particularly the discussion of simulated annealing. 5. The discussion of propositional logic and its limitations as an effective A.I. paradigm. 6. The treatment of first-order logic and its use in modeling simple reflex agents, change, and its use in situation calculus. There is a good overview of inference in first-order logic in chapter 9 of the book, including completeness and resolution. 7. The treatment of logic programming systems; the Prolog language is discussed as a logical programming language. Noting that Prolog cannot specify constraints on values, the authors discuss constraint logic programming (CLP) as an alternative logic programming language that allows constraints. 8. The discussion of semantic networks and description logics. 9.Read more ›
Most Recent Customer Reviews
I don't know why authors of AI and Machiner Learning books can not write their books explaining concepts in the most effective and efficient way, without using all the very... Read morePublished on February 7, 2013 by Rafael Carvajal Díaz
Very good reference book on main trends in artificial intelligence. It's a bit outdated but all the basis are here with very clear explanations and implementation examples in... Read morePublished on December 16, 2011 by Gaël Waiche
The book explains well the theory behind many AI techniques, but you only reaches the pseudocode, so people that are new to programming must resort to Google to get some better... Read morePublished on October 22, 2010 by Daniel Mosquera
The book gives a good introduction to the field and tries to explain the underlying problems and challenges and their possible solutions. Read morePublished on December 4, 2002
I am a Computer Science major at UGA and I can honestly say that this book is the absolute worst of any that I have ever read. Read morePublished on September 17, 2002
This book is useful as an introductory textbook. After reading this book, I produced an game playing system with randomized behavior that was able to beat very good (but not... Read morePublished on July 1, 2002
...I can only relate my experience from using this as a textbook for an introductory AI class with assigned topics. Read morePublished on April 7, 2002 by Hilbert
Not being an expert in this field, what I liked about this book was the wide range of topics it covered, sketching a history of the central ideas in each. Read morePublished on September 20, 2001 by Amazon Customer