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Keywords: Artificial Intelligence, machine learning

Title: Artificial Intelligence

Author: Rob Callan

Publisher: Palgrave

ISBN: 0333801369

Media: Book

Level: Introductory

Verdict: Wide scope, light on the maths but lacking depth in places

This is a single volume introduction to Artificial Intelligence aimed at beginning students or those who have a general interest in the topic. As such it attempts to cover a wide range of topics, from logic programming to genetic algorithms to Bayesian and neural networks. In short, to cover the broadest range of topics that fall under the AI umbrella.

The language tends not to be overly mathematical, and the book is relatively light on proofs and mathematical formalisms when compared to some other introductory books. Instead there is emphasis on algorithms and applications, with frequent uses of pseudo-code to illustrate a particular technique. However this isn't to say that it's possible to go from nought to working code with this book alone. Anyone wanting to implement genetic algorithms or neural networks, for example, is likely to require more depth than is available here.

The book is organised in seven sections, going from the introduction, to logic and search, uncertainty (mainly Bayesian networks), deciding on actions, learning (one of the most interesting sections of the book, covering neural networks, inductive logic programming and genetic algorithms), natural language understanding and perception, and finally closing with a look at agents, applications and the philosophical implications of AI.

While the book has wide scope it suffers in terms of depth of material. For example the section on genetic algorithms focuses almost exclusively on binary coded strings, does not look at problems in encoding hypotheses or at multi-objective evolutionary algorithms. Similarly the chapters on neural networks tend to cover a range of issues without going into much depth. However, given that this is meant to be an introductory volume this is to be expected.

Inevitably one has to contrast this book with a number of other single-volume introductions to AI, from Tom Mitchell's Machine Learning to Russell and Norvig's Artificial Intelligence: A Modern Approach. The latter titles have a similar or greater scope, and also a greater depth of detail. However the mathematical requirements of these are much greater. If you need something more than a relatively light introduction then one of these other titles would be more suitable. If, on the other hand, you need an over-view or enough to cover an AI module of a computer science course this might be good enough.


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