Negnevitsky shows students how to build intelligentsystems drawing on techniques from knowledge-based systems, neuralnetworks, fuzzy systems, evolutionary computation and now alsointelligent agents. The principles behind these techniques areexplained without resorting to complex mathematics, showing how thevarious techniques are implemented, when they are useful and whenthey are not. No particular programming language is assumed and thebook does not tie itself to any of the software tools available. However, available tools and their uses will be described andprogram examples will be given in Java. The lack of assumed priorknowledge makes this book ideal for any introductory courses inartificial intelligence or intelligent systems design, while thecontemporary coverage means more advanced students will benefit bydiscovering the latest state-of-the-art techniques.
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The course was, in my opinion, too short for the material covered. The book, however, appeared to be more promising. First, it is well-written and covers the "essentials" of AI such as expert systems, fuzzy logic, neural networks, genetic algorithms, hybrid intelligent systems and data mining.
Second, each chapter is well-organized with sufficient examples, a summary of key points and questions for review at the end. Third, at just over pages and being only around 9.
Fourth, the pages are bright white with crisp black text which also makes for easy reading even where lighting is not perfect. However, I do have a few issues with the book. First, it does not really cover things like Monte-Carlo search, the minimax algorithm used in chess or swarm intelligence, to name a few. The beginning of each chapter is seductive with its easy-going introduction and general overview, especially to the uninitiated, I would imagine.
However, the average reader I have advanced degrees in computer science, by the way will likely find himself trying to catch his breath after that. There is a little too much content squeezed into too few pages. Even more, Negnevitsky uses a considerable amount of mathematics, charts and diagrams which are not always easy understand.
It is assumed, of course, that the reader has a "basic" understanding of math. If "advanced" math is used in say, rocket science, "basic" is just a relative term.
If you simply skip over these things or assume they are true without trying hard to really understand them, you will not likely learn as much. I did not intend to read this book to relive my undergraduate course in AI but it put me through it nonetheless.
I was actually hoping for a less technical but sufficiently lucid explication of the different approaches currently used in AI; a "refresher" course, so to speak. Something that would explain the general principles without focusing too much on actual pen and paper calculations which are unnecessary, even if one works in AI, unless one actually plans to employ a particular approach; in which case they can pursue it further elsewhere.
In that respect, I was somewhat disappointed. This book appears to be intended mainly for undergraduates with the "be ready for the exam" mentality. To test this hypothesis, just see how many of the "questions for review", in total, that you can answer correctly after reading the whole book. Not to mention actually being able to do the kind of calculations the book seems to emphasize. To summarize the second issue, the book kind of pulls the reader away from gaining an important conceptual perspective of AI techniques and how they relate to each other.
This is still possible despite the undergraduate and generally technical nature of the book but you will have to be careful to see the forest for the trees. In certain cases, Negnevitsky seems to have forgotten that while this book was "developed from lectures to undergraduates" see the back cover , his readers are not necessarily attending those lectures afterward to ask for clarifications.
For instance, in Case Study 9, he mentions the Gini coefficient and says they were used in Figure 9. I, for one, was not previously familiar with it. The fourth issue is that I think there is also at least one significant error in the book in Figure 9. However, the figure seems to show that the network trained with noisy examples has a higher percentage of recognition error.
How is this an improvement? Fuzzy logic and neural networks seem to come up more often. This can be condoned to an extent but I really did not see the purpose of bringing up Adaptive Neuro-Fuzzy Inference Systems ANFIS as part of an "introductory text for a course in AI" and later referencing it in Case Study 8, which implies that it should be properly understood.
Perhaps it deserved better treatment in the context of this book. Genetic algorithms, on the other hand, was nicely explained and later made Case Study 7 relatively easy to understand. Finally, I have to say that the cover art does the book only further injustice. In summary, I would still recommend purchasing this book because some parts are beautifully explained and this is good for quick reference, especially when memory fails. Such a book may not be on the required reading list of undergraduate courses in AI or advanced courses in philosophy but it would probably be much more accessible to the public and even computer scientists in general.
Artificial Intelligence: A Guide to Intelligent
His research interests include power system security, power quality, reliability, distributed and renewable power generation, demand response management and smart grids. His research also involves the development and application of intelligent systems in power systems. Professor Negnevitsky has provided leadership in carrying out several externally funded research projects. Professor Negnevitsky has authored and co-authored more than refereed research publications including 71 journal papers, more than conference papers, 10 chapters in books, 2 books, 8 edited conference proceedings and received 4 patents for inventions. The aim of the project is to improve integration of renewable energy in remote and isolated communities.