Data analytics on the board game Go for the discovery of interesting sequences of moves in joseki
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Date
2018
Authors
Leung, Carson
Kanke, Felix
Cuzzocrea, Alfredo
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
In the current era of big data, high volumes of a wide variety of data of different veracity are generated at a high velocity in many real-life applications. Embedded in these big data is valuable information or knowledge. This calls for data science solution for discovering knowledge from the big data. A rich source of big data is game data. In this article, we focus on the board game of Go, which is a popular two-player strategic board game. Due to its popularity, many people are studying sequences of moves in games (i.e., joseki). However, with high volumes of the game data, manual solution or complex automatic solution for joseki may not be practical. Hence, in this article, we present a simple automatic data science solution for discovering interesting sequences of moves in joseki for the board game Go. Evaluation results show the benefits and practicality of using our solution in data analytics of the game.
Description
Keywords
data science, data mining, Go, joseki, board game, game mining, prefix tree, pruning
Citation
C.K. Leung, F. Kanke, A. Cuzzocrea. Data analytics on the board game Go for the discovery of interesting sequences of moves in joseki. Procedia Computer Science, 126 (2018), pp. 831-840