Chess2vec: Learning Vector Representations for Chess
arXiv:2011.01014v13 citations
AI Analysis
This is an incremental study for chess AI and game analysis, introducing a new approach to represent chess elements.
The paper tackled the problem of generating vector representations for chess pieces and moves, uncovering latent structures and predicting moves from positions, with preliminary results anticipating ongoing neural network work.
We conduct the first study of its kind to generate and evaluate vector representations for chess pieces. In particular, we uncover the latent structure of chess pieces and moves, as well as predict chess moves from chess positions. We share preliminary results which anticipate our ongoing work on a neural network architecture that learns these embeddings directly from supervised feedback.