Werewolf is a popular party game throughout the world, and research on its significance has progressed in recent years. The Werewolf game is based on conversation, and in order to win, participants must use all of their cognitive abilities. This communication game requires the playing agents to be very sophisticated to win. In this research, we generated a sophisticated agent to play the Werewolf game using a complex weighted ensemble learning approach. This research work aimed to estimate what other agents/players think of us in the game. The agent was developed by aggregating strategies of different participants in the AI Wolf competition and thereby learning from them using machine learning. Moreover, the agent created was able to perform much better than other competitors using very basic strategies to show the approach's effectiveness in the Werewolf game. The machine learning technique used here is not restricted to the Werewolf game but may be extended to any game that requires communication and action depending on other participants.

翻译：狼人是全世界流行的政党游戏,有关其重要性的研究近年来有所进展。狼人游戏以对话为基础,为了赢得胜利,参与者必须使用他们所有的认知能力。这种交流游戏要求游戏代理器非常精密才能获胜。在这个研究中,我们利用复杂的加权混合学习方法制作了一个精密的代理器来玩狼人游戏。这一研究工作旨在估计其他代理商/玩家在游戏中对我们的看法。该代理商是通过收集AI Wolf竞赛不同参与者的战略来开发的,从而通过机器学习向他们学习。此外,所创建的代理商能够比其他竞争者更出色地运用非常基本的策略来展示狼人游戏的方法的有效性。这里使用的机器学习技术不限于狼人游戏,但可以扩大到需要交流和采取行动的游戏,取决于其他参与者。