Introduction to Artificial Intelligence

by Wolfgang Ertel, Nathanael T. Black (Translator)

The ultimate aim of artificial intelligence (A.I.) is to understand intelligence and to build intelligent software and robots that come close to the performance of humans. On their way towards this goal, A.I. researchers have developed a number of quite different subdisciplines.

This concise and accessible Introduction to Artificial Intelligence supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The textbook presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning.

Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, theorems, and illustrative cartoons; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at the website http: //www.hs-weingarten.de/ ertel/aibook.

Students of computer science and other technical natural sciences will find this easy-to-read textbook excellent for self-study, a high-school level of knowledge of mathematics being the only prerequisite to understanding the material. With its extensive tools and bibliography, it is an ideal, quick resource on A.I.

Genres: Artificial Intelligence, Computer Science

316 pages, Paperback

First published January 1, 2008

Reviews

[Bojan Tunguz · Rating 4 out of 5]

Artificial Intelligence is by now a relatively old field, having originated in the early days of the digital computer revolution. However, it has had a very rocky and turbulent history, going through several cycles of overblown expectations followed by almost equally dramatic swings towards disillusionment and skepticism. In recent years, though, it has matured into a very solid and practical discipline that exercises an ever growing importance across a wide breadth of technologies and professions. We increasingly take speech recognition, handwriting recognition, and natural language search for granted. Basic familiarity with what Artificial Intelligence is, and what tools and techniques fall under its domain, are becoming ever important aspect of a variety of professions and occupations.

There is no shortage of books and resources on Artificial Intelligence. However, most of them fall squarely into two main camps: discursive overviews for the general audience, and highly advanced textbooks requiring deep familiarity with many advanced technical concepts. Ertel’s “Introduction to Artificial Intelligence,” even though it’s pretty technical in its own right, is still fairly accessible introduction to this field for anyone with solid grasp of basic college-level math and computer science concepts.

The book is organized somewhat chronologically along the lines of topics that have historically formed the main organizing principles for the study of Artificial Intelligence - first and second order logic, propositional calculus, PROLOG, machine learning, neural networks. Some of the earlier chapters’ material is a bit dated, and in some cases unfamiliar to students and practitioners in North America. For instance, it seems that PROLOG never quite got a hold on this side of Atlantic. There are a few more or less amusing examples of how quickly technology ages, such as references to Google Video links, which haven’t been around for a few years now. I would have also liked a substantially more material on machine learning and neural nets, maybe at the expense of the earlier chapters. These topics have a lot of practical applications today, and seem to be the guiding paradigms for Artificial Intelligence as a whole for a foreseeable future. Nonetheless, the book overall is very readable and relevant.

One of the most valuable aspects of this book are the worked out examples and numerous (solved) exercises. Working through problems is, by far, the best way to learn any new material, and this book provides the reader with numerous and wide-ranging opportunity to do exactly that.

Overall, this is a very well written and pedagogical book that fills an important niche in the Artificial Intelligence educational literature. Highly recommended.

**** Electronic version of the book provided by the publisher for review purposes. ****

[Kai Weber · Rating 3 out of 5]

Compared with the large standard coursebook on the subject of artificial intelligence ‒ Artificial Intelligence: A Modern Approach, Global Edition ‒ this book of Ertel is more challenging with respect to the math contained and the steep ascension between the topics and chapters. It was the author's aim to present a briefer and more affordable introduction, and I can imagine that this book serves well as a companion to a university course. But in spite of the many exercises, which also come with sample solutions in the appendix, it is not very conducive to self-study. There's neither enough practical application nor enough linkage between the chapters and AI approaches to motivate an autodidact.

[Sabine · Rating 4 out of 5]

An introduction to methods of AI. Useful for people who've already have some background knowledge in logic, since the basics aren't covered in much detail. Very concise descriptions; somewhat more practically oriented; includes short overview of PROLOG and machine learning techniques.

[Jack · Rating 2 out of 5]

Not what I was expecting.

This book spent a lot more time discussing logic and not doing a very good job of it. It’s been a long time since I did symbolic logic so I had a hard time remembering what the symbols and operators meant. As a result, I didn’t follow many of the authors discussions. Expensive book, not worth the price.

[Douglas S · Rating 2 out of 5]

Interesting, less practical and more background.

[Herman Slatman · Rating 4 out of 5]

Great introduction. Touches the surface of many algorithms and gives nice pointers to follow-up literature.

[Agjeliki Kaso · Rating 3 out of 5]

super