  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 few-shot learning architecture. We analyzed the model
performance via three virtual players, namely Nim Guru, Random player, and
Q-learner. In the second part, we applied the game learning language model to
the chess game, and a large set of grandmaster games with exhaustive
encyclopedia openings. Finally, we have shown that model practically learns the
rules of the chess game and can survive games against Stockfish at a category-A
rating level.
