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README.md

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 search.py: 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/test_search.py: 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 repl.it. You can run the code in an IDE, or from the command line with python -i filename.py 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 jupyter.org 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 https://github.com/aimacode/aima-python.git

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:

py.test

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

Acknowledgements

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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Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

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