A Machine Learning playing GO
- Sponsored by: Lehrstuhl für Geometrie und Visualisierung
- Faculty Lead: M.Sc. Bernhard Werner
- Project Lead: Dr. Ricardo Acevedo Cabra
- Semester: Winter 2017/2018
Overview
The game Go is perhaps one of the most challenging of the classical games for artificial intelligence due to its huge search space and the difficulty of evaluating moves and board positions. The goal of this project is to develop from scratch a learning machine, e.g., based on a neural network architecture, that plays at best GO on a reduced size board. The network would be trained with the results of playing against minimax algorithms of different moves and tree depths. For detailed info contact M.Sc. Bernhard Werner.
Results
The results of this project were summarized in a final presentation and explained in detail in the final documentations of GO group 1 and GO group 2