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]
Search
Search algorithms
Game-tree search
Optimization
Evolutionary and bio-inspired methods
Evolutionary computation
Bio-inspired methods
Reasoning and inference
Automated reasoning
Logic
Statistical inference
Planning and navigation
Pathfinding
Motion planning
Machine learning
Ensemble learning
Classification
- Alternating decision tree
- C4.5 algorithm
- CN2 algorithm
- Decision tree learning
- Dominance-based rough set approach
- Genetic Algorithm for Rule Set Production
- ID3 algorithm
- k-nearest neighbors algorithm
- Logic learning machine
- Logistic regression
- Naive Bayes classifier
- Relevance vector machine
- RIPPER
- Structured kNN
- Support vector machine
- Winnow algorithm[2]
Clustering
Association learning
Dimensionality reduction
Neural learning
Reinforcement learning
Deep learning
Other machine learning
Natural language processing
Computer vision
Algorithmic game play
See also
- Algorithm engineering
- Glossary of artificial intelligence
- List of algorithms
- List of artificial intelligence journals
- List of quantum algorithms
- List of robotics software
- Outline of algorithms
- Outline of artificial intelligence
- Lists of open-source artificial intelligence software
- TurboQuant – online vector quantization algorithm for compressing high-dimensional vectors in large language model inference
- AlphaDev, AlphaEvolve, AlphaTensor — AI systems by Google DeepMind for discovering and optimizing algorithms
References
- "Artificial Intelligence (AI) Algorithms". GeeksforGeeks. July 23, 2025. Retrieved April 19, 2026.
- "Machine Learning Algorithms". GeeksforGeeks. January 20, 2026. Retrieved April 19, 2026.
- Quesada, Alberto (October 28, 2019). "5 algorithms to train a neural network". Neural Designer Blog. Artelnics. Retrieved April 20, 2026.
- 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.
- Silver, David; et al. (October 2017). "Mastering the game of Go without human knowledge". Nature. 550: 354–359. doi:10.1038/nature24270.
- 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.
- 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.
- Tesauro, Gerald (March 1995). "Temporal difference learning and TD-Gammon". Communications of the ACM. 38 (3): 58–68. doi:10.1145/203330.203343.