aima-python
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
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Download Latest Version (.zip)- docs.yml
- notebooks-to-py.yml
- tests.yml
- __init__.py
- agents.py
- csp.py
- deep_learning.py
- game_theory.py
- games.py
- ipyviews.py
- knowledge.py
- learning.py
- logic.py
- making_simple_decisions.py
- mdp.py
- nlp.py
- notebook_utils.py
- perception.py
- planning.py
- probabilistic_learning.py
- probability.py
- reinforcement_learning.py
- search.py
- text.py
- utils.py
- agents.rst
- conf.py
- csp.rst
- games.rst
- index.rst
- language.rst
- learning.rst
- logic.rst
- planning.rst
- probability.rst
- requirements.txt
- searching.rst
- utils.rst
- eight_puzzle.py
- genetic_algorithm_example.py
- grid_mdp.py
- romania_problem.py
- tic-tac-toe.py
- tsp.py
- vacuum_agent.py
- xy_vacuum_environment.py
- -0.04.jpg
- -0.4.jpg
- -4.jpg
- 4.jpg
- aima3e_big.jpg
- aima_logo.png
- bayesnet.png
- broxrevised.png
- cake_graph.jpg
- decisiontree_fruit.jpg
- dirt.svg
- dirt05-icon.jpg
- ensemble_learner.jpg
- fig_5_2.png
- ge0.jpg
- ge1.jpg
- ge2.jpg
- ge4.jpg
- general_learning_agent.jpg
- grid_mdp.jpg
- grid_mdp_agent.jpg
- hillclimb-tsp.png
- IMAGE-CREDITS
- knn_plot.png
- knowledge_foil_family.png
- knowledge_foil_grandparent.png
- makefile
- maze.png
- mdp-a.png
- mdp-b.png
- mdp-c.png
- mdp-d.png
- mdp.png
- model_based_reflex_agent.jpg
- model_goal_based_agent.jpg
- model_utility_based_agent.jpg
- neural_net.png
- parse_tree.png
- perceptron.png
- plurality_learner_plot.png
- point_crossover.png
- pop.jpg
- queen_s.png
- random_forest.png
- refinement.png
- restaurant.png
- romania_map.png
- search_animal.svg
- simple_problem_solving_agent.jpg
- simple_reflex_agent.jpg
- sprinklernet.jpg
- stapler1-test.png
- uniform_crossover.png
- vacuum-icon.jpg
- vacuum.svg
- wall-icon.jpg
- canvas.js
- continuousworld.js
- gridworld.js
- agents.ipynb
- csp.ipynb
- game_theory.ipynb
- games.ipynb
- knowledge.ipynb
- learning.ipynb
- logic.ipynb
- mdp.ipynb
- nlp.ipynb
- planning.ipynb
- probability.ipynb
- reinforcement_learning.ipynb
- search.ipynb
- text.ipynb
- Welcome.ipynb
- build.sh
- jupyter-lite.json
- jupyter_lite_config.json
- README.md
- requirements-lite.txt
- verify_browser.py
- -0.04.jpg
- -0.4.jpg
- -4.jpg
- 4.jpg
- grid_mdp.jpg
- grid_mdp_agent.jpg
- maze.png
- mdp-a.png
- mdp-b.png
- mdp-c.png
- mdp-d.png
- mdp.png
- Algorithms for MDPs.ipynb
- Algorithms for MDPs.py
- Introduction.ipynb
- Introduction.py
- MDPs.ipynb
- MDPs.py
- Partially Observable MDP.ipynb
- Partially Observable MDP.py
- Sequential Decision Problems.ipynb
- Sequential Decision Problems.py
- decisiontree_fruit.jpg
- ensemble_learner.jpg
- knn_plot.png
- random_forest.png
- Datasets.ipynb
- Datasets.py
- Decision Tree.ipynb
- Decision Tree.py
- Ensemble Learning.ipynb
- Ensemble Learning.py
- Linear and Nonparametric Models.ipynb
- Linear and Nonparametric Models.py
- autoencoder.png
- backprop.png
- corss_entropy_plot.png
- mse_plot.png
- nn.png
- nn_steps.png
- perceptron.png
- rnn_connections.png
- rnn_unit.png
- rnn_units.png
- vanilla.png
- Learners.ipynb
- Learners.py
- Loss Functions and Layers.ipynb
- Loss Functions and Layers.py
- Optimizer and Backpropagation.ipynb
- Optimizer and Backpropagation.py
- RNN.ipynb
- RNN.py
- mdp.png
- Active Reinforcement Learning.ipynb
- Active Reinforcement Learning.py
- Passive Reinforcement Learning.ipynb
- Passive Reinforcement Learning.py
- parse_tree.png
- Grammar.ipynb
- Grammar.py
- Introduction.ipynb
- Introduction.py
- nlp_apps.ipynb
- nlp_apps.py
- Parsing.ipynb
- Parsing.py
- derivative_of_gaussian.png
- gradients.png
- laplacian.png
- laplacian_kernels.png
- RCNN.png
- stapler.png
- stapler_bbox.png
- Image Edge Detection.ipynb
- Image Edge Detection.py
- Image Segmentation.ipynb
- Image Segmentation.py
- Objects in Images.ipynb
- Objects in Images.py
- agents.ipynb
- agents.py
- arc_consistency_heuristics.ipynb
- arc_consistency_heuristics.py
- bootstrap.ipynb
- bootstrap.py
- classical_planning_approaches.ipynb
- classical_planning_approaches.py
- csp.ipynb
- csp.py
- dynamic_decision_network.ipynb
- dynamic_decision_network.py
- expectation_maximization.ipynb
- expectation_maximization.py
- game_theory.ipynb
- game_theory.py
- games.ipynb
- games.py
- improving_sat_algorithms.ipynb
- improving_sat_algorithms.py
- index.ipynb
- index.py
- intro.ipynb
- intro.py
- kalman_filter.ipynb
- kalman_filter.py
- knowledge_current_best.ipynb
- knowledge_current_best.py
- knowledge_foil.ipynb
- knowledge_foil.py
- knowledge_version_space.ipynb
- knowledge_version_space.py
- learning.ipynb
- learning.py
- learning_apps.ipynb
- learning_apps.py
- logic.ipynb
- logic.py
- mdp.ipynb
- mdp.py
- mdp_apps.ipynb
- mdp_apps.py
- neural_nets.ipynb
- neural_nets.py
- nlp.ipynb
- nlp.py
- nlp_apps.ipynb
- nlp_apps.py
- planning.ipynb
- planning.py
- planning_angelic_search.ipynb
- planning_angelic_search.py
- planning_graph_plan.ipynb
- planning_graph_plan.py
- planning_hierarchical_search.ipynb
- planning_hierarchical_search.py
- planning_partial_order_planner.ipynb
- planning_partial_order_planner.py
- planning_total_order_planner.ipynb
- planning_total_order_planner.py
- probability.ipynb
- probability.py
- reinforcement_learning.ipynb
- reinforcement_learning.py
- sarsa.ipynb
- sarsa.py
- search.ipynb
- search.py
- text.ipynb
- text.py
- vacuum_world.ipynb
- vacuum_world.py
- viterbi_algorithm.ipynb
- viterbi_algorithm.py
- __init__.py
- pytest.ini
- test_agents.py
- test_csp.py
- test_deep_learning.py
- test_game_theory.py
- test_games.py
- test_graphplan.py
- test_knowledge.py
- test_learning.py
- test_logic.py
- test_mdp.py
- test_nlp.py
- test_perception.py
- test_planning.py
- test_probabilistic_learning.py
- test_probability.py
- test_reinforcement_learning.py
- test_search.py
- test_text.py
- test_utils.py
- .coveragerc
- .flake8
- .gitignore
- .gitmodules
- aima-data
- CONTRIBUTING.md
- LICENSE
- pyproject.toml
- pytest.ini
- README.md
- requirements.txt
- SUBMODULE.md
# Installation Guide
1. Get the code
git clone https://github.com/aimacode/aima-python
Downloads the entire project code from GitHub to your computer.
cd aima-python
Moves into the project folder you just downloaded.
2. Python
Easy RecommendedPrerequisites
pip install -r requirements.txt
Installs the Python libraries listed in requirements.txt (or similar).
If it runs without errors and prints output in the terminal, it worked.
Pulled directly from this repo's README.
3. Make
MediumPrerequisites
- Git Needed to download the project code from GitHub.
- Make Usually pre-installed on Linux/macOS. On Windows, install separately (e.g. via MSYS2 or WSL).
cd images
This project's files live in a subfolder, so move into it first.
make
Compiles the code based on the generated build configuration to produce an executable.
If it finishes without errors, it worked. Try running the generated executable directly.
// repository documentation
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