AI Subfields

Large difficult problems are frequently solved by first breaking them up into a set of interrelated smaller problems.  The AI subfields can be a set of such smaller problems into which AI is decomposed.  It is also useful to have a set of subfields that you can go to in order to find methods, algorithms, etc. that can be helpful in your AI work.  No such list is ever complete or unique but here is one I use:

1. weak methods
2. search
3. rules
4. semantic nets
5. logic/deduction
6. heuristics
7. discovery/creativity/induction
8. natural language
9. neural networks
10. distributed AI/collective intelligence
11. robotics/embodiment
12. compression
13. automata/state machines
14. statistics
15. Bayesian statistics
16. planning/scheduling
17. case-based reasoning/memory-based reasoning
18. blackboard systems
19. nonstandard logics (spatial logics, temporal logics, higher order logics, multivalued logics, etc.)
20. representations
21. consciousness
22. learning/data mining
23. theorem proving
24. automatic programming
25. genetic programming
26. qualitative reasoning
27. constraint-based reasoning
28. agents
29. fuzzy logic
30. diagrammatic reasoning (and spatial logic)
31. model-based reasoning
32. emotion
33. ontology
34. quantum computing
35. analogy
36. parallel computing
37. pattern recognition/comparison
38. causality
39. deductive databases
40. language of thought
41. artificial life
42. philosophy of AI and mind
43. innateness/instinct
44. AI languages
45. memory/databases
46. decision theory
47. cognitive science
48. control system theory
49. digital electronics/hardware
50. dynamical systems
51. self-organizing systems
52. perception/vision/image manipulation (and spatial logic)
53. architectures
54. complexity theory
55. emergence
56. brain modeling
57. modularity
58. hybrid AI
59. optimization
60. goal-oriented systems
61. feature extraction/detection
62. utility/values/fitness/progress
63. multivariate function approximation
64. formal grammars and languages
65. theory of computation
66. classifiers/concept formation
67. theory of problem solving
68. artificial immune systems
69. curriculum for learner
70. speech recognition
71. theory of argumentation/informal logic
72. common sense reasoning
73. coherence/consistency
74. relevance/sensitivity analysis
75. semiotics
76. machine translation
77. pattern theory
78. operations research
79. game theory
80. automation
81. behaviorism
82. knowledge engineering
83. semantic web
84. sorting/typology/taxonomy
85. extrapolation/forecasting/interpolation/generalization
86. cooperation theory
87. systems theory
88. semantic computing
89. exploratory programming
90. specialization/decomposition

There is, of course, lots of overlap in these.  Some are certainly more important than others but each might be
a source of help/information/inspiration. (My AI research library is organized into these 89 clusters of books/papers, plus some miscellaneous, textbooks, etc.)