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List of artificial intelligence algorithms

From Wikipedia, the free encyclopedia

This is a list of artificial intelligence algorithms, including algorithms and algorithmic methods used in artificial intelligence (AI) for search, automated reasoning, knowledge representation and reasoning, planning, machine learning, deep learning, natural language processing, computer vision, and related areas.[1]

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Search algorithms

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Optimization

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Evolutionary and bio-inspired methods

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Evolutionary computation

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Bio-inspired methods

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Reasoning and inference

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Automated reasoning

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Logic

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Statistical inference

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Planning and navigation

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Pathfinding

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Motion planning

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Machine learning

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Ensemble learning

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Classification

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Clustering

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Association learning

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Dimensionality reduction

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Neural learning

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Reinforcement learning

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Deep learning

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Other machine learning

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Natural language processing

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Computer vision

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Algorithmic game play

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See also

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References

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  1. ↑ "Artificial Intelligence (AI) Algorithms". GeeksforGeeks. July 23, 2025. Retrieved April 19, 2026.
  2. ↑ "Machine Learning Algorithms". GeeksforGeeks. January 20, 2026. Retrieved April 19, 2026.
  3. ↑ Quesada, Alberto (October 28, 2019). "5 algorithms to train a neural network". Neural Designer Blog. Artelnics. Retrieved April 20, 2026.
  4. ↑ Silver, David; et al. (January 2016). "Mastering the game of Go with deep neural networks and tree search". Nature. 529 (7587): 484–489. doi:10.1038/nature16961.
  5. ↑ Silver, David; et al. (October 2017). "Mastering the game of Go without human knowledge". Nature. 550: 354–359. doi:10.1038/nature24270.
  6. ↑ Silver, David; et al. (December 2018). "A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play". Science. 362 (6419): 1140–1144. doi:10.1126/science.aar6404.
  7. ↑ Schrittwieser, Julian; et al. (December 2020). "Mastering Atari, Go, chess and shogi by planning with a learned model". Nature. 588: 604–609. arXiv:1911.08265. doi:10.1038/s41586-020-03051-4.
  8. ↑ Tesauro, Gerald (March 1995). "Temporal difference learning and TD-Gammon". Communications of the ACM. 38 (3): 58–68. doi:10.1145/203330.203343.