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Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition), by Michael Negnevitsky
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Artificial Intelligence is one of the most rapidly evolving subjects within the computing/engineering curriculum, with an emphasis on creating practical applications from hybrid techniques. Despite this, the traditional textbooks continue to expect mathematical and programming expertise beyond the scope of current undergraduates and focus on areas not relevant to many of today's courses. Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents. The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not. No particular programming language is assumed and the book does not tie itself to any of the software tools available. However, available tools and their uses will be described and program examples will be given in Java. The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contempory coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques.
- Sales Rank: #560322 in Books
- Brand: Brand: Addison-Wesley
- Published on: 2004-11-12
- Ingredients: Example Ingredients
- Original language: English
- Number of items: 1
- Dimensions: 9.38" h x 1.07" w x 6.38" l, 1.72 pounds
- Binding: Hardcover
- 440 pages
Review
“This book covers many areas related to my module. I would be happy to recommend this book to my students. I believe my students would be able to follow this book without any difficulty. Book chapters are very well organised and this will help me to pick and choose the subjects related to this module.” Dr Ahmad Lotfi, Nottingham Trent University, UK
From the Back Cover
[Shelving Category] Artificial Intelligence/Soft Computing
Artificial Intelligence is often perceived as being a highly complicated, even frightening subject in Computer Science. This view is compounded by books in this area being crowded with complex matrix algebra and differential equations - until now. This book, evolving from lectures given to students with little knowledge of calculus, assumes no prior programming experience and demonstrates that most of the underlying ideas in intelligent systems are, in reality, simple and straightforward. Are you looking for a genuinely lucid, introductory text for a course in A.I or Intelligent Systems Design? Perhaps you¿re a non-computer science professional looking for a self-study guide to the state-of-the art in knowledge based systems? Either way, you can¿t afford to ignore this book.
Covers:
· Rule-based expert systems
· Fuzzy expert systems
· Frame-based expert systems
· Artificial neural networks
· Evolutionary computation
· Hybrid intelligent systems
· Knowledge engineering
· Data mining
New to this edition:
· New demonstration rule-based system, MEDIA ADVISOR
· New section on genetic algorithms
· Four new case studies
· Completely updated to incorporate the latest developments in this fast-paced field.
Dr Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia. The book has developed from lectures to undergraduates. Its material has also been extensively tested through short courses introduced at Otto-von-Guericke-Universit¿t Magdeburg, Institut Elektroantriebstechnik, Magdeburg, Germany, Hiroshima University, Japan and Boston University and Rochester Institute of Technology, USA
Educated as an electrical engineer, Dr Negnevitsky¿s many interests include artificial intelligence and soft computing. His research involves the development and application of intelligent systems in electrical engineering, process control and environmental engineering. He has authored and co-authored over 250 research publications including numerous journal articles, four patents for inventions and two books.
About the Author
Negnevitsky is with the University of Tasmania, Australia.
Most helpful customer reviews
19 of 20 people found the following review helpful.
Excellent Treatment of Complex Topics
By Mario Missakian
What Dr. Negnevitsky states in the preface of this book, "Most of the literature on AI is expressed in the jargon of computer science, and crowded with complex matrix algebra and differential equations" is an accurate assessment of current textbooks that try to go beyond just the basics of AI.
Actually, this book does contain some of the same complex material that Dr. Negnevitsky accuses others for having with one exception: He does a terrific job in simplifying the complex theories behind them.
At first, when I flipped through the pages, huge equations and matrices jumped at me. My first impression was that this book was for serious computer scientists or mathematicians. I was looking for simpler material for my beginning AI students. I started reading the preface and found the argument interesting.
I speed-read through the first chapter and found the history of the field presented in a concise and a very well laid out fashion. I jumped into reading the beginning of chapter 2 and I was amazed at how well Dr. Negnevitsky progressed from basic ideas to more and more complex layers. With other similar books, the reader will need many basic theory books (mathematics, basic AI...) in order to understand the topics. Dr. Negnevitsky provides all the basics necessary. This same strategy is repeated for the remaining chapters.
I acquired the book and read it from beginning to end. I found the material consistently well presented. One warning: this book does get very technical and complex in many chapters. However, the material in each of those chapters is progressively laid out. Even if a reader stops in the middle of some chapters, there is still a lot to gain from the experience of reading the entire book. I highly recommend it to anyone interested in really understanding beyond just keywords and delve into the internals of AI topics.
Thanks to Dr. Negnevitsky for a great book.
13 of 14 people found the following review helpful.
Great Introductory Book on Soft Computing
By Omolade Saliu
For a beginner that wants to know where the stories about Soft Computing really converge, this book is a starting point. The style of the author is simple and great.
My interest was to get a book that keeps the daunting mathematical jargons in Fuzzy Logic (contained in several other books) minimal, while presenting the concepts. I fell in love with this book, that I had to run through all the pages as if it's a novel.
This book really demonstrates that the whole idea behind intelligent systems are simple and straightforward. You do not need another teacher. He presented algorithms (e.g. back-propagation)in a very simple to understand manner.
Dr. Michael Negnevitsky, the author, must be a great teacher. It's a handy and nice book. I strongly recommend it.
11 of 12 people found the following review helpful.
A very good introductory text book for intelligent systems
By Paras Jethwani
The author explains various AI concepts in very simple terms and has managed to present the math behind some of the ideas in an understandable manner.
The treatment of various topics is intermediate though but it is a good place to start and does not leave the reader riddled with complex math equations.
In-fact the author has done a great job at keeping the concepts separate from the mathematics, except for some places like neural networks where it is not possible to explain the concepts without talking about the math involved.
Instead of focusing too much on a particular aspect of intelligent systems this book deals with a whole spectrum of technologies such as fuzzy systems, neural networks, hybrid systems etc.
The writing style of the author is very simple and clear and it is possible to finish the entire book over a period of one semester or a little more.
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