A Novel Weighted Ensemble Learning Based Agent for the Werewolf Game
This work addresses the challenge of building effective agents for communication-based games like Werewolf, which is incremental as it applies ensemble learning to an existing competition setting.
The researchers tackled the problem of creating a sophisticated agent for the Werewolf game by developing a weighted ensemble learning approach that aggregates strategies from participants in the AI Wolf competition, resulting in an agent that performed much better than competitors using basic strategies.
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.