Automatic Classification of Remarks in Werewolf BBS
Takanori Fukui, Keisuke Ando, Toshihide Murakami, Nobuhiro Ito, Kazunori Iwata · 2017
In the recent years, the evolution of artificial intelligence (AI) has influenced the development of human-interactive communication games. The Werewolf game was originally a real-world, face-to-face, indoor game played between a minimum of four players. In this research, we focus on Werewolf BBS, which is a web-based game, based on the real-life Werewolf game. In Werewolf BBS, players can send various messages among themselves, including a wide variety of chat messages and discussions on current affairs. Thus, it is challenging for AI to understand the phrases being passed on by players. The aim of this study is to understand the content of the messages being communicated in Werewolf BBS. In this paper, we have introduce an automatic classification method based on machine learning to categorize the phrases into six groups. Previous studies have used unavailable information that must be excluded when introducing AI to the game. Thus, we hereby propose a method to classify phrases without additional information using a support vector machine (SVM). SVMs are discriminative classifier models of machine learning. In this paper, an SVM is used as a tool for categorization; it discriminates the different expressions shared by the players into the six groups. The results of an experiment confirm that the messages can be classified without preconditions. This verifies the effectiveness of the present research.