What is aima-python?

This project provides working Python versions of the algorithms described in the textbook "Artificial Intelligence: A Modern Approach." It lets you actually run and experiment with the concepts from the book, rather than just reading about them. The project is organized by topic, things like search, logic, probability, and learning. Each topic gets a Python file with the algorithm implementations, a Jupyter notebook that explains how they work with examples, and a test file to verify the code is correct. There is also a large table that maps each piece of code back to the specific figure and page where it appears in the textbook, so you can cross-reference what you are running with what you are reading. Students and self-learners working through the textbook are the main audience. If you are taking an AI course that uses the book, you can use this repository to see how the pseudocode translates into actual runnable Python. The notebook files are particularly handy for that, since they walk through examples step by step. The project is a work in progress. The table shows many algorithms that are already implemented, but also plenty of entries that are still blank, meaning no one has written the code for them yet. The maintainers actively invite contributors to fill in those gaps, and they mention looking for students to participate through Google Summer of Code. So beyond just using the code, someone learning AI could practice their Python skills by implementing one of the missing pieces. One thing to note is that the index table is partially outdated, having been built for an older edition of the book and only partially updated since. That means the mapping between figures and code is not perfectly current, which could be a minor frustration if you are trying to follow along with the latest edition.