论文标题
狼人游戏的新型加权合奏学习代理
A Novel Weighted Ensemble Learning Based Agent for the Werewolf Game
论文作者
论文摘要
狼人是全球流行的派对游戏，近年来对其意义的研究已经发展。狼人游戏基于对话，为了获胜，参与者必须使用他们所有的认知能力。这种沟通游戏要求比赛代理人非常精致才能获胜。在这项研究中，我们生成了一个复杂的代理商，使用复杂的加权合奏学习方法来玩狼人游戏。这项研究工作旨在估算游戏中其他代理商/玩家对我们的看法。该代理是通过汇总AI Wolf竞争中不同参与者的策略而开发的，从而使用机器学习向他们学习。此外，创建的代理商能够使用非常基本的策略表现出该方法在狼人游戏中的有效性。此处使用的机器学习技术不仅限于狼人游戏，但可以扩展到任何需要沟通和行动的游戏，具体取决于其他参与者。
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.
