Artificial Intelligence: A Guide to Intelligent Systems

[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 you're a non-computer science professional looking for a self-study guide to the state-of-the art in knowledge based systems? Either way, you can't 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-Universit't Magdeburg, Institut Elektroantriebstechnik, Magdeburg, Germany, Hiroshima University, Japan and Boston University and Rochester Institute of Technology, USA 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 250 research publications including numerous journal articles, four patents for inventions and two books.

Table of Contents:
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