Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information

TL;DR

The paper tackles enabling LLMs to cooperate in a multi-player imperfect-information card game (Guandan) using Theory of Mind planning. It proposes a ToM planning framework that lets LLM agents anticipate and adapt to both allies and adversaries using only game rules, current state, and historical context. An external tool is integrated to manage the large and dynamic action space inherent in the game. Experimental results show a performance gap with state-of-the-art RL but confirm ToM capabilities and robust improvements against opposing agents, with code released for reproducibility.

Abstract

Large language models (LLMs) have shown success in handling simple games with imperfect information and enabling multi-agent coordination, but their ability to facilitate practical collaboration against other agents in complex, imperfect information environments, especially in a non-English environment, still needs to be explored. This study investigates the applicability of knowledge acquired by open-source and API-based LLMs to sophisticated text-based games requiring agent collaboration under imperfect information, comparing their performance to established baselines using other types of agents. We propose a Theory of Mind (ToM) planning technique that allows LLM agents to adapt their strategy against various adversaries using only game rules, current state, and historical context as input. An external tool was incorporated to mitigate the challenge of dynamic and extensive action spaces in this card game. Our results show that although a performance gap exists between current LLMs and state-of-the-art reinforcement learning (RL) models, LLMs demonstrate ToM capabilities in this game setting. It consistently improves their performance against opposing agents, suggesting their ability to understand the actions of allies and adversaries and establish collaboration with allies. To encourage further research and understanding, we have made our codebase openly accessible.