Turing Test via Emergent Communication in the Game of Werewolf

Emergent communication can lead to more efficient problem-solving heuristics and more domain specificity than a handcrafted communication protocol, potentially directing autonomous agents towards unforeseen yet effective solutions. Previous research has investigated a social deduction game, called Werewolf, where two groups of autonomous agents, villagers and werewolves, interact in an environment named RLupus. We extend it to allow the agents to communicate through multiple rounds and evaluate their language and performance against the baseline environment. We show that agents develop a highly successful heuristic using a single word vocabulary. They create an approach analogous to the Turing test allowing them to determine which agents are werewolves, which is the winning condition. Our experimental analysis shows that our approach speeds up the convergence of the agents towards effective communication strategies.