The aim of my project is to write implementations and visualizations for algorithms in the book 'AI: A Modern Approach'. This includes a couple of new algorithms being added to the 4th edition. IPython notebooks with interactive visualizations were also made with the aim of providing intuitive explanations of the algorithms. Pytest tests were added for all the implemented algorithms and also for previous modules missing tests.
The link to all my work that has been merged into the main repository is here. Reserve link
- Forward Chaining algorithm for FOL queries
- Improved UI for Canvas TicTacToe against AI
- Visualization for Backward Chaining algorithm
- Random Forest
- Truncated Singular Value Decomposition
- Monte Carlo localization
- First Order Inductive Learner
- Added information about Permutation Decoders and Knowledge Base to notebook
The python algorithms can be run directly in a python session by downloading and importing the necessary modules. It is easier to use them by running the corresponding .ipynb Jupyter notebook. Doing this also lets you run the interactive visualizations.
- More usage examples and interactions can be added to the notebooks.
- Implement the Wumpus World environment and make it interactive.
- Improve
expr()parsing and efficiently handleExprinstances with multiple arguments. - Reduce dependencies like networkx by adding the required functionality to canvas.py