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An introduction to knowledge engineering
Simon K., Malcolm C., Springer-Verlag New York, Inc., Secaucus, NJ, 2006. 300 pp. Type: Book (9781846284755)
Date Reviewed: Mar 1 2007

The field of knowledge-based systems is in need of a book that accounts for its latest advances. This book, however, fails to fulfill its premise. To start, it reads like a set of lecture notes rather than a textbook. While it has a very well-defined list of essential topics of knowledge engineering, there is very little text explaining these topics. In fact, this leads to some confusion in chapter 2, section 2, covering neural networks (page 44). While supervised and unsupervised learning are very briefly explained, with some examples, other examples from both techniques are presented in a way that may confuse the reader, given the diagram on page 45. Another example, also on page 44, is the explanation of adaptive resonance theory. It never goes beyond explaining why it was developed, and the fact that it is an example of unsupervised learning. The details of the theory itself, simplified though they may be, do not seem to follow.

On the positive side, the book is written in a modern style, which should encourage undergraduate students to read it. Each chapter starts with a very short introduction, followed by bullet point “Objectives.” The book also provides Web site links to timely and current research projects. Finally, there are activities throughout the book, which replace the traditional questions and answers at the end of chapters, seen in older textbooks.

Overall, the book covers all classical topics of knowledge-based systems: knowledge acquisition, representation, expert system shells, and life cycles. It could make a good textbook for undergraduate courses, provided the lectures extend the topics, and the students research the uniform resource locator (URL) links provided.

Reviewer:  Aladdin Ayesh Review #: CR133988 (0804-0341)
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Knowledge Representation Formalisms And Methods (I.2.4 )
Expert System Tools And Techniques (I.2.5 ... )
Heuristic Methods (I.2.8 ... )
Deduction And Theorem Proving (I.2.3 )
Learning (I.2.6 )
Models Of Computation (F.1.1 )
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