فهرست مطالب
Table of Contents
1. Introduction 7
2. Uninformed Search 9
3. Informed Search 14
4. Constraint Satisfaction Problems 20
5. Knowledge Representation 27
6. Classical Logic Systems 35
7. Fuzzy Logic 42
8. Planning 46
9. Probabilistic Reasoning 53
10. Classification and Regression 59
11. Ensembles Methods 66
12. Clustering 72
13. Feature Selection 77
14. Biologically-Inspired Optimization 81
15. Local Optimization 86
16. Gradient-Based Optimization 93
17. Neural Networks: Multi-Layer Perceptrons 98
18. Support Vector Machines 106
19. Convolutional Neural Networks 110
20. Energy-Based Neural Models 116
21. Autoencoders and Variational Autoencoders 121
22. Recurrent Neural Networks 126
23. Self-Organizing Networks 131
24. Generative Adversarial Networks 137
25. Graph Neural Networks 142
26. Transformers 146
27. Natural Language Processing 151
28. Explainable Artificial Intelligence 157
29. Reinforcement Learning 161
30. Multi-Agent Systems 170
31. Sequential Games 177
32. Strategic Games 183
33. Cooperative Games 188
34. Auctions 192
35. Voting 195
36. Negotiation 200
37. Contract Net Protocol 204
38. Mechanism Design 207
39. Coordination 210
40. Multi-Agent Reinforcement Learning 214
41. Neuro-Symbolic Artificial Intelligence 219
42. Hierarchical Temporal Memory and the A Thousand Brains Theory 224
43. Cognitive Architectures 230
44. Fundamental Questions on Human and Artificial Intelligence 236
44.1. What Is Intelligence? An AI Perspective 236
44.2. What Is Intelligence? A Philosophical Perspective 239
44.3. What Is Artificial General Intelligence? 242
44.4. Can Machines Think? 246
44.5. What Is the Relationship Between AI and Human Intelligence? 249
44.6. What Are the Limits of AI? 251
44.7. How Should We Approach the “Black Box” Problem in AI? 253
44.8. What Are the Ethical Implications of AI? 256
44.9. How Should We Balance the Benefits and Risks of AI? 258
44.10. How Should AI Be Developed and Regulated? 260
45. Conclusions 263
Abbreviations 265
1. Introduction 7
2. Uninformed Search 9
3. Informed Search 14
4. Constraint Satisfaction Problems 20
5. Knowledge Representation 27
6. Classical Logic Systems 35
7. Fuzzy Logic 42
8. Planning 46
9. Probabilistic Reasoning 53
10. Classification and Regression 59
11. Ensembles Methods 66
12. Clustering 72
13. Feature Selection 77
14. Biologically-Inspired Optimization 81
15. Local Optimization 86
16. Gradient-Based Optimization 93
17. Neural Networks: Multi-Layer Perceptrons 98
18. Support Vector Machines 106
19. Convolutional Neural Networks 110
20. Energy-Based Neural Models 116
21. Autoencoders and Variational Autoencoders 121
22. Recurrent Neural Networks 126
23. Self-Organizing Networks 131
24. Generative Adversarial Networks 137
25. Graph Neural Networks 142
26. Transformers 146
27. Natural Language Processing 151
28. Explainable Artificial Intelligence 157
29. Reinforcement Learning 161
30. Multi-Agent Systems 170
31. Sequential Games 177
32. Strategic Games 183
33. Cooperative Games 188
34. Auctions 192
35. Voting 195
36. Negotiation 200
37. Contract Net Protocol 204
38. Mechanism Design 207
39. Coordination 210
40. Multi-Agent Reinforcement Learning 214
41. Neuro-Symbolic Artificial Intelligence 219
42. Hierarchical Temporal Memory and the A Thousand Brains Theory 224
43. Cognitive Architectures 230
44. Fundamental Questions on Human and Artificial Intelligence 236
44.1. What Is Intelligence? An AI Perspective 236
44.2. What Is Intelligence? A Philosophical Perspective 239
44.3. What Is Artificial General Intelligence? 242
44.4. Can Machines Think? 246
44.5. What Is the Relationship Between AI and Human Intelligence? 249
44.6. What Are the Limits of AI? 251
44.7. How Should We Approach the “Black Box” Problem in AI? 253
44.8. What Are the Ethical Implications of AI? 256
44.9. How Should We Balance the Benefits and Risks of AI? 258
44.10. How Should AI Be Developed and Regulated? 260
45. Conclusions 263
Abbreviations 265