Artificial Intelligence: A Guide to Intelligent Systems (3e)

Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents. 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.

• A new chapter on Data Mining and Knowledge Discovery, which introduces data mining as an integral part of knowledge discovery in large databases and considers main techniques and tools for turning data into knowledge.
• New case studies included on clustering with a self-organising neural network and data mining
• Updated throughout to include new developments in the discipline

• 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.

Dr Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia. The book has developed from his lectures to undergraduates. Educated as an electrical engineer, Dr Negnevitsky’s many interests include artificial intelligence and soft computing. His research involves the development and application of intelligent systems in electrical engineering, process control and environmental engineering. He has authored and co-authored over 300 research publications including numerous journal articles, four patents for inventions and two books.