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.
Features
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 To This Edition
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.
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
