Addison-Wesley / Prentice Hall
Computer Science
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Artificial Intelligence: A Guide to Intelligent Systems, 2/E
ISBN-10: 0321204662
ISBN-13: 9780321204660
Publisher: Addison-Wesley
Copyright: 2005
Format: Cloth; 440 pp
Published: 11/02/2004
Status: Instock
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 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. However, available tools and their uses will be described and program examples will be given in Java. 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.
- 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, PowerPoints and solutions to exercises.
- New case studies covering classification neural networks, genetic algorithms and neuro-fuzzy systems.
- A replacement case study "Media Advisor" showing a demonstration rule-based expert system.
1 Introduction To Knowledge-Based Intelligent Systems
1.1 Intelligent Machines, Or What Machines Can Do
1.2 The History Of Artificial Intelligence, Or From The Dark Ages
To Knowledge-Based Systems
1.3 Summary
Questions For Review
References
2 Rule-Based Expert Systems
2.1 Introduction, Or What Is Knowledge?
2.2 Rules As A Knowledge Representation Technique
2.3 The Main Players In The Expert System Development Team
2.4 Structure Of A Rule-Based Expert System
2.5 Fundamental Characteristics Of An Expert System
2.6 Forward Chaining And Backward Chaining Inference Techniques
2.7 MEDIA ADVISOR: A Demonstration Rule-Based Expert System
2.8 Conflict Resolution
2.9 Advantages And Disadvantages Of Rule-Based Expert Systems
2.10 Summary
Questions For Review
References
3 Uncertainty Management In Rule-Based Expert Systems
3.1 Introduction, Or What Is Uncertainty?
3.2 Basic Probability Theory
3.3 Bayesian Reasoning
3.4 FORECAST: Bayesian Accumulation Of Evidence
3.5 Bias Of The Bayesian Mesod
3.6 Certainty Factors Theory And Evidential Reasoning
3.7 FORECAST: An Application Of Certainty Factors
3.8 Comparison Of Bayesian Reasoning And Certainty Factors
3.9 Summary
Questions For Review
References
4 Fuzzy Expert Systems
4.1 Introduction, Or What Is Fuzzy Thinking?
4.2 Fuzzy Sets
4.3 Linguistic Variables And Hedges
4.4 Operations Of Fuzzy Sets
4.5 Fuzzy Rules
4.6 Fuzzy Inference
4.7 Building A Fuzzy Expert System
4.8 Summary
Questions For Review
References
Bibliography
5 Frame-Based Expert Systems
5.1 Introduction, Or What Is A Frame?
5.2 Frames As A Knowledge Representation Technique
5.3 Inference In Frame-Based Experts
5.4 Methods And Demons
5.5 Interaction Of Frames And Rules
5.6 Buy Smart: A Frame-Based Expert System
5.7 Summary
Questions For Review
References
Bibliography
6 Artificial Neural Networks
6.1 Introduction, Or How The Brain Works
6.2 The Neuron As A Simple Computing Element
6.3 The Perceptron
6.4 Multilayer Neural Networks
6.5 Accelerated Learning In Multilayer Neural Networks
6.6 The Hopfield Network
6.7 Bidirectional Associative Memories
6.8 Self-Organising Neural Networks
6.9 Summary
Questions For Review
References
7 Evolutionary Computation
7.1 Introduction, Or Can Evolution Be Intelligent?
7.2 Simulation Of Natural Evolution
7.3 Genetic Algorithms
7.4 Why Genetic Algorithms Work
7.5 Case Study: Maintenance Scheduling With Genetic Algorithms
7.6 Evolutionary Strategies
7.7 Genetic Programming
7.8 Summary
Questions For Review
References
8 Hybrid Intelligent Systems
8.1 Introduction, Or How To Combine German Mechanics With Italian Love
8.2 Neural Expert Systems
8.3 Neuro-Fuzzy Systems
8.4 ANFIS: Adaptive Neuro-Fuzy Inference System
8.5 Evolutionary Neural Networks
8.6 Fuzzy Evolutionary Systems
8.7 Summary
Questions For Review
References
9 Knowledge Engineering And Data Mining
9.1 Introduction, Or What Is Knowledge Engineering?
9.2 Will An Expert System Work For My Problem?
9.3 Will A Fuzzy Expert System Work For My Problem?
9.4 Will A Neural Network Work For My Problem?
9.5 Will Genetic Algorithms Work For My Problem?
9.6 Will A Neuro-Fuzzy System Work For My Problem?
9.7 Data Mining And Knowledge Discovery
9.8 Summary
Questions For Review
References
Glossary
Appendix
Index
[Shelving Category] Artificial Intelligence/Soft Computing
Artificial Intelligence is often perceived as being a highly complicated, even frightening subject in Computer Science. This view is compounded by books in this area being crowded with complex matrix algebra and differential equations - until now. This book, evolving from lectures given to students with little knowledge of calculus, assumes no prior programming experience and demonstrates that most of the underlying ideas in intelligent systems are, in reality, simple and straightforward. Are you looking for a genuinely lucid, introductory text for a course in A.I or Intelligent Systems Design? Perhaps youre a non-computer science professional looking for a self-study guide to the state-of-the art in knowledge based systems? Either way, you cant afford to ignore this 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
New to this edition:
· New demonstration rule-based system, MEDIA ADVISOR
· New section on genetic algorithms
· Four new case studies
· Completely updated to incorporate the latest developments in this fast-paced field.
Dr Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia. The book has developed from lectures to undergraduates. Its material has also been extensively tested through short courses introduced at Otto-von-Guericke-Universitt Magdeburg, Institut Elektroantriebstechnik, Magdeburg, Germany, Hiroshima University, Japan and Boston University and Rochester Institute of Technology, USA
Educated as an electrical engineer, Dr Negnevitskys 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 250 research publications including numerous journal articles, four patents for inventions and two books.
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