Learning Chess With Language Models and Transformers. (arXiv:2209.11902v1 [cs.AI])

Representing a board game and its positions by text-based notation enables
the possibility of NLP applications. Language models, can help gain insight
into a variety of interesting problems such as unsupervised learning rules of a
game, detecting player behavior patterns, player attribution, and ultimately
learning the game to beat state of the art. In this study, we applied BERT
models, first to the simple Nim game to analyze its performance in the presence
of noise in a setup of a …

arxiv chess language language models transformers