List of artificial intelligence algorithms
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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
- A* search algorithm
- Beam search
- Beam stack search
- Best-first search
- Breadth-first search
- Depth-first search
- General Problem Solver
- Iterative deepening A*
- Iterative deepening depth-first search
- Uniform-cost search
Game-tree search
Optimization
- ALOPEX
- Bayesian optimization
- Conjugate gradient method
- Gradient descent
- Hill climbing
- Levenberg–Marquardt algorithm
- Quadratic unconstrained binary optimization
- Quasi-Newton method
- Simulated annealing
- Stochastic gradient descent
Evolutionary and bio-inspired methods
Evolutionary computation
Bio-inspired methods
Reasoning and inference
Automated reasoning
Logic
Statistical inference
- Baum–Welch algorithm
- Belief propagation
- Expectation–maximization algorithm
- Forward–backward algorithm
- Kalman filter
- Markov chain Monte Carlo (MCMC)
- Viterbi algorithm
Planning and navigation
Pathfinding
Motion planning
- Bug algorithm
- Dynamic window approach
- Graphplan
- Probabilistic roadmap
- Rapidly-exploring random tree
- Vector Field Histogram
Machine learning
Ensemble learning
- AdaBoost
- Bootstrap aggregating
- BrownBoost
- Gradient boosting
- LogitBoost
- LPBoost
- Random forest
- Randomized weighted majority algorithm
- Weighted majority algorithm
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
- DBSCAN
- Hierarchical clustering
- k-means++
- k-means clustering
- k-medoids
- Linde–Buzo–Gray algorithm
- Lloyd's algorithm
- OPTICS
Association learning
- Apriori algorithm
- Eclat algorithm
- FP-growth algorithm
Dimensionality reduction
- Diffusion map
- FastICA
- Kernel principal component analysis
- Manifold alignment
- Minimum redundancy feature selection
- Non-negative matrix factorization
- Principal component analysis
- Sparse PCA
- T-distributed stochastic neighbor embedding
Neural learning
- Almeida–Pineda recurrent backpropagation
- Backpropagation
- GeneRec
- Generalized Hebbian algorithm
- Growing self-organizing map
- Leabra
- Learning vector quantization
- Perceptron
- Quickprop
- Rprop
- Self-organizing map
- Wake-sleep algorithm[3]
Reinforcement learning
- Actor-critic algorithm
- Constructing skill trees
- Error-driven learning
- Policy gradient method
- Prefrontal cortex basal ganglia working memory
- Proximal policy optimization
- PVLV
- Q-learning
- Skill chaining
- State–action–reward–state–action
- Temporal difference learning
Deep learning
Other machine learning
Natural language processing
- Byte-pair encoding
- Cocke–Younger–Kasami algorithm
- Earley parser
- Inside-outside algorithm
Computer vision
Algorithmic game play
See also
 |
Wikibooks has a book on the topic of: Algorithms |
- 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". 2025-07-23. https://www.geeksforgeeks.org/artificial-intelligence/ai-algorithms/.
- ↑ "Machine Learning Algorithms". 2026-01-20. https://www.geeksforgeeks.org/machine-learning/machine-learning-algorithms/.
- ↑ Quesada, Alberto (28 October 2019). "5 algorithms to train a neural network". Artelnics. https://www.neuraldesigner.com/blog/5_algorithms_to_train_a_neural_network/.
- ↑ Silver, David (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 (October 2017). "Mastering the game of Go without human knowledge". Nature 550: 354–359. doi:10.1038/nature24270.
- ↑ Silver, David (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 (December 2020). "Mastering Atari, Go, chess and shogi by planning with a learned model". Nature 588: 604–609. 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.
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