Presentation information

Organized Session

Organized Session » OS-20

[2F4-OS-20a] OS-20 (1)

Wed. Jun 10, 2020 1:50 PM - 3:30 PM Room F (jsai2020online-6)

大槻 恭士(山形大学)、狩野 芳伸(静岡大学)、片上 大輔(東京工芸大学)、大澤 博隆(筑波大学)、稲葉 将通(電気通信大学)

3:10 PM - 3:30 PM

[2F4-OS-20a-04] Estimating Roles with Utterance Vectors in Werewolf Game

〇Harunobu Tsukamoto1, Hidefumi Ohmura1, Kouichi Katsurada1 (1. Graduate School of Science and Technology, Tokyo University of Science)

Keywords:Werewolf Game, role estimation, LSTM

Werewolf Game is a team game in which several secret roles are given to the players and the players try to estimate other players’ role through discussion. For developing the game agents, some studies have used the Long Short-Term Memory (LSTM) which is appropriate for handling the sequence of utterances. The inputs given to LSTM in these studies are usually the one-hot vectors of the utterances. However, they have a problem that the dimension of the input vector increases when the variety of utterance increases. In this paper, we propose a game agent that represents the utterances with the embedded vectors derived from the word2vec algorithm. We also implemented the game agent that estimates all players’ roles. The experimental results show that we can reduce the dimension of the utterance vectors without decreasing the role estimation accuracy.

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