Learning on Graphs in the Game of Go

  title={Learning on Graphs in the Game of Go},
  author={Thore Graepel and Mike Goutri{\'e} and Marco Kr{\"u}ger and Ralf Herbrich},
We consider the game of Go from the point of view of machine learning and as a well-deened domain for learning on graph representations. We discuss the representation of both board positions and candidate moves and introduce the common fate graph (CFG) as an adequate representation of board positions for learning. Single candidate moves are represented as feature vectors with features given by subgraphs relative to the given move in the CFG. Using this representation we train a support vector… CONTINUE READING
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