Artificial Intelligence: A Guide To Intelligent Systems, 3rd edition

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 data mining.

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 MATLAB. The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contemporary coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques.

The 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

• No mathematical or programming prerequisites. 
• Linked coverage of all the latest artificial intelligence topics.
• Question and answer format.
• Accompanying website including student projects, accompanying software tools, software demonstrations, PowerPoint slides and solutions to exercises.

• The main objective of the book remains the same as in the first edition – to provide the reader with practical understanding of the field of computer intelligence. It is intended as an introductory text suitable for a one-semester course, and assumes the students have only limited knowledge of calculus and little or no programming experience.

  In terms of the coverage, this edition introduces a new chapter on data mining and demonstrates several new applications of intelligent tools for solving complex real-world problems. The major changes are as follows:

  ·         In the new chapter, ‘Data mining and knowledge discovery’, we introduce data mining as an integral part of knowledge discovery in large databases. We consider the main techniques and tools for turning data into knowledge, including statistical methods, data visualisation tools, Structured Query Language, decision trees and market basket analysis. We also present several case studies on data mining applications.

  ·         In Chapter 9, we add a new case study on clustering with a self-organising neural network.

  Finally, we have expanded the book’s references and bibliographies, and updated the list of AI tools and vendors in the appendix.