Artificial Intelligence A Guide to Intelligent Systems 2nd edition by Michael Negnevitsky 1408225751 9781408225752

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ISBN-10 :  1408225751 

ISBN-13 :  9781408225752

Author :  Michael Negnevitsky 

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 are described, and program examples are given in Java.

Artificial Intelligence A Guide to Intelligent Systems 2nd Table of contents:

1. Introduction to knowledge based intelligent systems
2. Intelligent machines, or what machines can do
3. The history of artificial intelligence, or from the ‘Dark Ages’ to knowledge-based systems
4. Summary
5. Questions for review
6. References
7. Rule-based expert systems
8. Introduction, or what is knowledge?
9. Rules as a knowledge representation technique
10. The main players in the expert system development team
11. Structure of a rule-based expert system
12. Fundamental characteristics of an expert system
13. Forward chaining and backward chaining inference techniques
14. MEDIA ADVISOR: a demonstration rule-based expert system
15. Conflict resolution
16. Advantages and disadvantages of rule-based expert systems
17. Summary
18. Questions for review
19. References
20. Uncertainty management in rule-based expert systems
21. Introduction, or what is uncertainty?
22. Basic probability theory
23. Bayesian reasoning
24. FORECAST: Bayesian accumulation of evidence
25. Bias of the Bayesian method
26. Certainty factors theory and evidential reasoning
27. FORECAST: an application of certainty factors
28. Comparison of Bayesian reasoning and certainty factors
29. Summary
30. Questions for review
31. References
32. Fuzzy expert systems
33. Introduction, or what is fuzzy thinking?
34. Fuzzy sets
35. Linguistic variables and hedges
36. Operations of fuzzy sets
37. Fuzzy rules
38. Fuzzy inference
39. Building a fuzzy expert system
40. Summary
41. Questions for review
42. References
43. Bibliography
44. Frame-based expert systems
45. Introduction, or what is a frame?
46. Frames as a knowledge representation technique
47. Inheritance in frame-based systems
48. Methods and demons
49. Interaction of frames and rules
50. Buy Smart: a frame-based expert system
51. Summary
52. Questions for review
53. References
54. Bibliography
55. Artificial neural networks
56. Introduction, or how the brain works
57. The neuron as a simple computing element
58. The perceptron
59. Multilayer neural networks
60. Accelerated learning in multilayer neural networks
61. The Hopfield network
62. Bidirectional associative memory
63. Self-organising neural networks
64. Summary
65. Questions for review
66. References
67. Evolutionary computation
68. Introduction, or can evolution be intelligent?
69. Simulation of natural evolution
70. Genetic algorithms
71. Why genetic algorithms work
72. Case study: maintenance scheduling with genetic algorithms
73. Evolution strategies
74. Genetic programming
75. Summary
76. Questions for review
77. References
78. Bibliography
79. Hybrid intelligent systems
80. Introduction, or how to combine German mechanics with Italian love
81. Neural expert systems
82. Neuro-fuzzy systems
83. ANFIS: Adaptive Neuro-Fuzzy Inference System
84. Evolutionary neural networks
85. Fuzzy evolutionary systems
86. Summary
87. Questions for review
88. References
89. Knowledge engineering
90. Introduction, or what is knowledge engineering?
91. Will an expert system work for my problem?
92. Will a fuzzy expert system work for my problem?
93. Will a neural network work for my problem?
94. Will genetic algorithms work for my problem?
95. Will a hybrid intelligent system work for my problem?
96. Summary
97. Questions for review
98. References
99. Data mining and knowledge discovery
100. Introduction, or what is data mining?
101. Statistical methods and data visualisation
102. Principal component analysis
103. Relational databases and database queries
104. The data warehouse and multidimensional data analysis
105. Decision trees
106. Association rules and market basket analysis

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