Chazzbot

Chess AI, using a feed-forward neural network as a value function and some simple minimax tree search. Build with Keras and Tensorflow trained on Grandmaster championship games fetched from https://www.pgnmentor.com/files.html.

The project uses a flask server as an interface and python-chess up to v.0.23.x for chess related stuff.

The project now supports playing games against the Sunfish.

The project is using Tensorflow 1.13.1 and Keras 2.2.4

Manual

• python3 data_extractor.py, on run converts all .pgn data contained in the folder data to the model's internal format. Stores the converted data in the folder ext as json.
• python3 move_predictor.py -t <name>, trains a new model with the data in ext and stores the model in model under the given model name
• python3 move_predictor.py -pg <name>, initiates a game for the AI to play against itself. Uses the model with the name given as argument.
• python3 move_predictor.py -s <name>, launches as simple rest api for clients to request predictions. Uses the model with the name given as argument.
• python3 move_predictor.py -sun <name>, starts a game against the ai Sunfish which have been included into this project.

A working model has been added to the project called model and can simply be used to run the application. The uploaded setup searches for 2 seconds by default, though this can be changed.

Potential improvements

• Apply some search algorithm to find the best move quicker.
• Add some alpha-beta pruning.
• Add support for castling and so on for the back-end.
• Improve and optimize the minimax search
• Potentially implement Monte Carlo tree search
• Improve the neural network accuracy
• Add support for python-chess 2.5+