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Large problems are usually solved by first breaking them up into a set of smaller problems. It is also useful to know where to go to find methods, algorithms, etc. that may be useful in your AI work. No list of subfields is ever complete and unique but here is one I use:
1. weak methods
2. search
3. rule based systems
4. semantic networks
5. logic/deduction systems
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 (including temporal logic)
20. representation
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 (including spatial logics)
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
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 learners
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
There is, of course, lots of overlap between these. Some are, of course, more fundamental to AI than others.
What else can I help you with?
Intelligence testing spawned a new avenue in the study of psychology known as?
Intelligence testing spawned a new avenue in the study of psychology known as
When was A.I. Artificial Intelligence - album - created?
A.I. Artificial Intelligence - album - was created in 2001.
When was Journal of Artificial Intelligence Research created?
The **Journal of Artificial Intelligence Research (JAIR)** was created in **1993**. It was one of the first scientific journals to be distributed online and is an open-access, peer-reviewed journal covering research in all areas of artificial intelligence.
When was Association for the Advancement of Artificial Intelligence created?
Association for the Advancement of Artificial Intelligence was created in 1979.
When was Artificial Intelligence II created?
Artificial Intelligence II was created on 1994-05-30.
What has the author Nils J Nilsson written?
Nils J. Nilsson has written: 'Learning machines' -- subject(s): Artificial intelligence 'The mathematical foundations of learning machines' -- subject(s): Artificial intelligence, Machine learning 'Artificial Intelligence' -- subject(s): Artificial intelligence
What is the meaning of LISP in artificial intelligence?
The meaning of LISP in artificial intelligence means Locator Identifier Separation Protocol.
When was Artificial Intelligence - compilation album - created?
Artificial Intelligence - compilation album - was created on 1992-07-09.
What are components of artificial intelligence in detail?
Artificial Intelligence (AI) is made up of several important components that work together to help machines **learn, understand, reason, make decisions, and interact with the world**. The major components include: ([CIET][1]) **Machine Learning (ML)** – Enables computers to learn patterns from data and improve their performance without being explicitly programmed for every task. Examples include recommendation systems and spam detection. **Deep Learning** – A type of machine learning that uses multi-layered artificial neural networks to process complex information. It is widely used in image recognition, speech recognition, and modern generativ **Natural Language Processing (NLP)** – Helps computers understand, interpret, and generate human language. Chatbots, translation tools, and voice assistants use NLP. **Computer Vision** – Allows machines to understand and analyze images and videos. It is used for facial recognition, object detection, medical-image analysis, and autonomous vehicles. **Robotics** – Combines AI with mechanical and control systems to create machines that can sense their environment and perform physical tasks. Robots can be used in manufacturing, healthcare, exploration, and other fields. **Expert Systems** – These systems use a knowledge base and rules to imitate the decision-making ability of a human expert in a specific field. They can provide recommendations, diagnoses, or decision support. **Knowledge Representation and Reasoning** – Enables AI systems to organize information and use it to draw conclusions, solve problems, and make decisions. **Planning and Decision-Making** – Helps AI determine what actions to take to achieve a particular goal. This is important in areas such as autonomous systems and robotics.
When was International Journal on Artificial Intelligence Tools created?
International Journal on Artificial Intelligence Tools was created in 1992.
ai?
Artificial Intelligence
AI?
Artificial Intelligence
