About: The Go Transformer: Natural Language Modeling for Game Play

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	The Go Transformer: Natural Language Modeling for Game Play
Creator	Kalin, Josh Noever, David Ciolino, Matt
source	ArXiv
abstract	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 (2020) 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).
has issue date	2020-07-07(xsd:dateTime)
has license	arxiv
sha1sum (hex)	f3fe535f1908f7acf473569e15503eb8ac706880
resource representing a document's title	The Go Transformer: Natural Language Modeling for Game Play
resource representing a document's body	covid:f3fe535f1908f7acf473569e15503eb8ac706880#body_text
is schema:about of	named entity 'model' named entity 'game' named entity 'Amazons' named entity 'moves' named entity 'CORNER' »more»

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