  The Werewolf game is a social deduction game based on free natural language
communication, in which players try to deceive others in order to survive. An
important feature of this game is that a large portion of the conversations are
false information, and the behavior of artificial intelligence (AI) in such a
situation has not been widely investigated. The purpose of this study is to
develop an AI agent that can play Werewolf through natural language
conversations. First, we collected game logs from 15 human players. Next, we
fine-tuned a Transformer-based pretrained language model to construct a value
network that can predict a posterior probability of winning a game at any given
phase of the game and given a candidate for the next action. We then developed
an AI agent that can interact with humans and choose the best voting target on
the basis of its probability from the value network. Lastly, we evaluated the
performance of the agent by having it actually play the game with human
players. We found that our AI agent, Deep Wolf, could play Werewolf as
competitively as average human players in a villager or a betrayer role,
whereas Deep Wolf was inferior to human players in a werewolf or a seer role.
These results suggest that current language models have the capability to
suspect what others are saying, tell a lie, or detect lies in conversations.
