Abstract
Emergent communication can lead to more efficient problem-solving heuristics and more domain specificity. It can perform better 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 study the impact of allowing 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 using passwords, allowing them to determine which agents are werewolves, which is the winning condition. We explore the possible reasons behind this strategy, with further experimental analysis showing that our approach speeds up the convergence of the agents towards a common communication strategy.
Olaf Lipinski
PhD Student in Artificial Intelligence
I study how autonomous agents develop langauge from scratch.