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.

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.).

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.

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.

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!

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!

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.

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.