Project developed for a university exam in 2019

The aim of this project is to train and test differents sub-symbolic systems capable to play the mastermind board game. To do this, this repository contains:

• Players that implement different mathematical strategies to play the game and build the training database
• Neural networks implemented using Tensorflow Estimators
• Jupyter notebooks to manage the databases
• Jupyter notebooks to analyze the neural networks performances

• This is my first ML project and the first "big" project in a more general sense
• I haven't changed anything from the exam submission, and I haven't plan to improve this package
• Keypoints
  • Mastermind players to build the ML dataset:
    • knuth - best algorithm, from Knuth paper
    • knuth_fast - my idea to speed-up knuth algorithm
    • hopeful - simple but fast algorithm
  • All the dataset are available on mega
    • After unzip, those are the file:

    ❯ tree -s -h --du
    .
    ├── [ 99M]  hopeful
    │   ├── [ 69M]  hopeful.csv
    │   └── [ 30M]  hopeful_cuts.csv
    ├── [133K]  knuth
    │   ├── [ 89K]  knuth_optimal.csv
    │   └── [ 40K]  knuth_optimal_cuts.csv
    └── [ 19M]  knuth_fast
        ├── [5.8M]  knuth_cuts.csv
        └── [ 13M]  knuth_fast.csv
    
     118M used in 3 directories, 6 files
    

  • Machine learning models
    • The model is a Bi-LSTM with attention system
    • The model is written in Tensorflow using the tf_estimator (TF2 was not released when I did the exam).
    • For the best solution, 4 models were trained and used in cascade (see the slides)
  • Slides are available for a deeper explanation of the project.
    • Although in Italian, they use a lot of images