Aima Python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
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Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
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aima-python Build Status Binder

Python code for the book Artificial Intelligence: A Modern Approach. You can use this in conjunction with a course on AI, or for study on your own. We're looking for solid contributors to help.

Updates for 4th Edition

The 4th edition of the book as out now in 2020, and thus we are updating the code. All code here will reflect the 4th edition. Changes include:

  • Move from Python 3.5 to 3.7.
  • More emphasis on Jupyter (Ipython) notebooks.
  • More projects using external packages (tensorflow, etc.).

Structure of the Project

When complete, this project will have Python implementations for all the pseudocode algorithms in the book, as well as tests and examples of use. For each major topic, such as search, we provide the following files:

  • search.ipynb and Implementations of all the pseudocode algorithms, and necessary support functions/classes/data. The .py file is generated automatically from the .ipynb file; the idea is that it is easier to read the documentation in the .ipynb file.
  • search_XX.ipynb: Notebooks that show how to use the code, broken out into various topics (the XX).
  • tests/ A lightweight test suite, using assert statements, designed for use with py.test, but also usable on their own.

Python 3.7 and up

The code for the 3rd edition was in Python 3.5; the current 4th edition code is in Python 3.7. It should also run in later versions, but does not run in Python 2. You can install Python or use a browser-based Python interpreter such as You can run the code in an IDE, or from the command line with python -i where the -i option puts you in an interactive loop where you can run Python functions. All notebooks are available in a binder environment. Alternatively, visit for instructions on setting up your own Jupyter notebook environment.

Features from Python 3.6 and 3.7 that we will be using for this version of the code:

  • f-strings: all string formatting should be done with f'var = {var}', not with 'var = {}'.format(var) nor 'var = %s' % var.
  • typing module: declare functions with type hints: def successors(state) -> List[State]:; that is, give type declarations, but omit them when it is obvious. I don't need to say state: State, but in another context it would make sense to say s: State.
  • Underscores in numerics: write a million as 1_000_000 not as 1000000.
  • dataclasses module: replace namedtuple with dataclass.

Installation Guide

To download the repository:

git clone

Then you need to install the basic dependencies to run the project on your system:

cd aima-python
pip install -r requirements.txt

You also need to fetch the datasets from the aima-data repository:

git submodule init
git submodule update

Wait for the datasets to download, it may take a while. Once they are downloaded, you need to install pytest, so that you can run the test suite:

pip install pytest

Then to run the tests:


And you are good to go!

Index of Algorithms

Here is a table of algorithms, the figure, name of the algorithm in the book and in the repository, and the file where they are implemented in the repository. This chart was made for the third edition of the book and is being updated for the upcoming fourth edition. Empty implementations are a good place for contributors to look for an issue. The aima-pseudocode project describes all the algorithms from the book. An asterisk next to the file name denotes the algorithm is not fully implemented. Another great place for contributors to start is by adding tests and writing on the notebooks. You can see which algorithms have tests and notebook sections below. If the algorithm you want to work on is covered, don't worry! You can still add more tests and provide some examples of use in the notebook!

Figure Name (in 3rd edition) Name (in repository) File Tests Notebook
2 Random-Vacuum-Agent RandomVacuumAgent Done Included
2 Model-Based-Vacuum-Agent ModelBasedVacuumAgent Done Included
2.1 Environment Environment Done Included
2.1 Agent Agent Done Included
2.3 Table-Driven-Vacuum-Agent TableDrivenVacuumAgent Done Included
2.7 Table-Driven-Agent TableDrivenAgent Done Included
2.8 Reflex-Vacuum-Agent ReflexVacuumAgent Done Included
2.10 Simple-Reflex-Agent SimpleReflexAgent Done Included
2.12 Model-Based-Reflex-Agent ReflexAgentWithState Done Included
3 Problem Problem Done Included
3 Node Node Done Included
3 Queue Queue Done No Need
3.1 Simple-Problem-Solving-Agent SimpleProblemSolvingAgent Done Included
3.2 Romania romania Done Included
3.7 Tree-Search depth/breadth_first_tree_search Done Included
3.7 Graph-Search depth/breadth_first_graph_search Done Included
3.11 Breadth-First-Search breadth_first_graph_search Done Included
3.14 Uniform-Cost-Search uniform_cost_search Done Included
3.17 Depth-Limited-Search depth_limited_search Done Included
3.18 Iterative-Deepening-Search iterative_deepening_search Done Included
3.22 Best-First-Search best_first_graph_search Done Included
3.24 A*-Search astar_search Done Included
3.26 Recursive-Best-First-Search recursive_best_first_search Done Included
4.2 Hill-Climbing hill_climbing Done Included
4.5 Simulated-Annealing simulated_annealing Done Included
4.8 Genetic-Algorithm genetic_algorithm Done Included
4.11 And-Or-Graph-Search and_or_graph_search Done Included
4.21 Online-DFS-Agent online_dfs_agent Done Included
4.24 LRTA*-Agent LRTAStarAgent Done Included
5.3 Minimax-Decision minimax_decision Done Included
5.7 Alpha-Beta-Search alphabeta_search Done Included
6 CSP CSP Done Included
6.3 AC-3 AC3 Done Included
6.5 Backtracking-Search backtracking_search Done Included
6.8 Min-Conflicts min_conflicts Done Included
6.11 Tree-CSP-Solver tree_csp_solver Done Included
7 KB KB Done Included
7.1 KB-Agent KB_AgentProgram Done Included
7.7 Propositional Logic Sentence Expr Done Included
7.10 TT-Entails tt_entails Done Included
7.12 PL-Resolution pl_resolution Done Included
7.14 Convert to CNF to_cnf Done Included
7.15 PL-FC-Entails? pl_fc_entails Done Included
7.17 DPLL-Satisfiable? dpll_satisfiable Done Included
7.18 WalkSAT WalkSAT Done Included
7.20 Hybrid-Wumpus-Agent HybridWumpusAgent
7.22 SATPlan SAT_plan Done Included
9 Subst subst Done Included
9.1 Unify unify Done Included
9.3 FOL-FC-Ask fol_fc_ask Done Included
9.6 FOL-BC-Ask fol_bc_ask Done Included
10.1 Air-Cargo-problem air_cargo Done Included
10.2 Spare-Tire-Problem spare_tire Done Included
10.3 Three-Block-Tower three_block_tower Done Included
10.7 Cake-Problem have_cake_and_eat_cake_too Done Included
10.9 Graphplan GraphPlan Done Included
10.13 Partial-Order-Planner PartialOrderPlanner Done Included
11.1 Job-Shop-Problem-With-Resources job_shop_problem Done Included
11.5 Hierarchical-Search hierarchical_search Done Included
11.8 Angelic-Search angelic_search Done Included
11.10 Doubles-tennis double_tennis_problem Done Included
13 Discrete Probability Distribution ProbDist Done Included
13.1 DT-Agent DTAgent Done Included
14.9 Enumeration-Ask enumeration_ask Done Included
14.11 Elimination-Ask elimination_ask Done Included
14.13 Prior-Sample prior_sample Done Included
14.14 Rejection-Sampling rejection_sampling Done Included
14.15 Likelihood-Weighting likelihood_weighting Done Included
14.16 Gibbs-Ask gibbs_ask Done Included
15.4 Forward-Backward forward_backward Done Included
15.6 Fixed-Lag-Smoothing fixed_lag_smoothing Done Included
15.17 Particle-Filtering particle_filtering Done Included
16.9 Information-Gathering-Agent InformationGatheringAgent Done Included
17.4 Value-Iteration value_iteration Done Included
17.7 Policy-Iteration policy_iteration Done Included
17.9 POMDP-Value-Iteration pomdp_value_iteration Done Included
18.5 Decision-Tree-Learning DecisionTreeLearner Done Included
18.8 Cross-Validation cross_validation*
18.11 Decision-List-Learning DecisionListLearner*
18.24 Back-Prop-Learning BackPropagationLearner Done Included
18.34 AdaBoost AdaBoost Done Included
19.2 Current-Best-Learning current_best_learning Done Included
19.3 Version-Space-Learning version_space_learning Done Included
19.8 Minimal-Consistent-Det minimal_consistent_det Done Included
19.12 FOIL FOIL_container Done Included
21.2 Passive-ADP-Agent PassiveADPAgent Done Included
21.4 Passive-TD-Agent PassiveTDAgent Done Included
21.8 Q-Learning-Agent QLearningAgent Done Included
22.1 HITS HITS Done Included
23 Chart-Parse Chart Done Included
23.5 CYK-Parse CYK_parse Done Included
25.9 Monte-Carlo-Localization monte_carlo_localization Done Included

Index of data structures

Here is a table of the implemented data structures, the figure, name of the implementation in the repository, and the file where they are implemented.

Figure Name (in repository) File
3.2 romania_map
4.9 vacumm_world
4.23 one_dim_state_space
6.1 australia_map
7.13 wumpus_world_inference
7.16 horn_clauses_KB
17.1 sequential_decision_environment
18.2 waiting_decision_tree


Many thanks for contributions over the years. I got bug reports, corrected code, and other support from Darius Bacon, Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others. Now that the project is on GitHub, you can see the contributors who are doing a great job of actively improving the project. Many thanks to all contributors, especially @darius, @SnShine, @reachtarunhere, @antmarakis, @Chipe1, @ad71 and @MariannaSpyrakou.

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