The cost of passing -- using deep learning AIs to expand our understanding of the ancient game of Go

Autor:	Egri-Nagy, Attila, Törmänen, Antti
Rok vydání:	2022
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	AI engines utilizing deep learning neural networks provide excellent tools for analyzing traditional board games. Here we are interested in gaining new insights into the ancient game of Go. For that purpose, we need to define new numerical measures based on the raw output of the engines. In this paper, we develop a numerical tool for automated move-by-move performance evaluation in a context-sensitive manner and for recognizing game features. We measure the urgency of a move by the cost of passing, which is the score value difference between the current configuration of stones and after a hypothetical pass in the same board position. Here we investigate the properties of this measure and describe some applications. Comment: 10 pages, 3 figures, final version will be published elsewhere
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2208.12643 Zobrazit plný text záznamu View this record from Arxiv