Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Game
This addresses the challenge of enabling AI agents to collaborate effectively in dynamic, language-driven scenarios, though it is incremental in improving existing methods.
The study tackled the problem of ad hoc teamwork in complex gaming environments like Avalon, where agents must cooperate without established protocols, and found that LLM agents show potential but suffer from hallucinations in communication, with CodeAct improving adaptation by using enhanced memory and code-driven reasoning.
Multi-agent collaboration with Large Language Models (LLMs) demonstrates proficiency in basic tasks, yet its efficiency in more complex scenarios remains unexplored. In gaming environments, these agents often face situations without established coordination protocols, requiring them to make intelligent inferences about teammates from limited data. This problem motivates the area of ad hoc teamwork, in which an agent may potentially cooperate with a variety of teammates to achieve a shared goal. Our study focuses on the ad hoc teamwork problem where the agent operates in an environment driven by natural language. Our findings reveal the potential of LLM agents in team collaboration, highlighting issues related to hallucinations in communication. To address this issue, we develop CodeAct, a general agent that equips LLM with enhanced memory and code-driven reasoning, enabling the repurposing of partial information for rapid adaptation to new teammates.