  This work applies natural language modeling to generate plausible strategic
moves in the ancient game of Go. We train the Generative Pretrained Transformer
(GPT-2) to mimic the style of Go champions as archived in Smart Game Format
(SGF), which offers a text description of move sequences. The trained model
further generates valid but previously unseen strategies for Go. Because GPT-2
preserves punctuation and spacing, the raw output of the text generator
provides inputs to game visualization and creative patterns, such as the Sabaki
project's game engine using auto-replays. Results demonstrate that language
modeling can capture both the sequencing format of championship Go games and
their strategic formations. Compared to random game boards, the GPT-2
fine-tuning shows efficient opening move sequences favoring corner play over
less advantageous center and side play. Game generation as a language modeling
task offers novel approaches to more than 40 other board games where historical
text annotation provides training data (e.g., Amazons & Connect 4/6).
