aimacode/aima-python

★ 8,732Jupyter NotebookAudience · researcherComplexity · 2/5Setup · easy

Mindmap

mindmap
  root((aima-python))
    What it is
      Textbook companion code
      4th edition AIMA
      Learning reference
    Topics
      Search algorithms
      Logic and planning
      Machine learning
      Probabilistic reasoning
    Format
      Python source files
      Jupyter notebooks
      pytest test suite
    Audience
      AI students
      Self-learners

mindmap root((aima-python)) What it is Textbook companion code 4th edition AIMA Learning reference Topics Search algorithms Logic and planning Machine learning Probabilistic reasoning Format Python source files Jupyter notebooks pytest test suite Audience AI students Self-learners

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Things people build with this

USE CASE 1

Run the code implementation of any AI algorithm from the Russell and Norvig textbook alongside the chapter where it is described.

USE CASE 2

Use the Jupyter notebooks to interactively step through and visualize classic AI algorithms like A*, minimax, or Bayesian networks.

USE CASE 3

Extend or contribute implementations for algorithms that are not yet fully coded or tested in the repository.

USE CASE 4

Verify that local modifications to algorithm implementations are correct by running the included pytest test suite.

Tech stack

PythonJupyter Notebookpytest

Getting it running

Difficulty · easy Time to first run · 30min

Requires Python 3.7+, pip dependencies, and a companion dataset repository cloned separately.

No license information is mentioned in the explanation.

In plain English

This repository contains Python code that goes alongside the textbook "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig, which is one of the most widely used university textbooks on AI. The repository provides working Python implementations of the algorithms described in the book, so that students and self-learners can read the pseudocode in the text and then run actual code to see how each algorithm behaves. The project is organized by topic, mirroring the chapters of the book. For topics like search, planning, logic, learning, and probabilistic reasoning, there are corresponding Python files and Jupyter notebooks. Jupyter notebooks are interactive documents that mix explanatory text, code, and output in one place, making them well suited for learning. The notebooks let you run code examples step by step and see the results immediately without setting up a full programming environment. The code targets Python 3.7 and later versions and does not support Python 2. Setup involves cloning the repository, installing dependencies via pip, and pulling in a companion dataset repository. A test suite is included and can be run using a tool called pytest to verify that the implementations are working correctly. The README includes a large index table listing every algorithm from the textbook, which file implements it, whether tests exist for it, and whether a notebook section covers it. This makes it easy to find a specific algorithm from the book and jump straight to its code or explanation. The project is associated with the 4th edition of the textbook, published in 2020. It welcomes contributions, particularly for algorithms that are not yet fully implemented or that lack tests and notebook coverage. This is primarily a learning and reference resource rather than a production library.

Copy-paste prompts

Prompt 1

Using aimacode/aima-python, show me how to run the A* search algorithm on a simple grid problem and visualize the path it finds.

Prompt 2

I'm studying Bayesian networks from the Russell and Norvig textbook. Which file in aima-python implements this and how do I create a simple network and run inference?

Prompt 3

How do I set up the aimacode/aima-python environment, install dependencies, and run the test suite to confirm everything works?

Prompt 4

Walk me through the minimax algorithm implementation in aima-python. Which file is it in and how do I run it on a Tic-Tac-Toe game?

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