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

• URL: http://arxiv.org/abs/2408.02559v1
• Date: Mon, 5 Aug 2024 15:36:46 GMT
• Title: Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information
• Authors: Yauwai Yim, Chunkit Chan, Tianyu Shi, Zheye Deng, Wei Fan, Tianshi Zheng, Yangqiu Song,
• Abstract summary: Large language models (LLMs) have shown success in handling simple games with imperfect information. This study investigates the applicability of knowledge acquired by open-source and API-based LLMs to sophisticated text-based games.
• Score: 36.11862095329315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Cooperation on the Fly: Exploring Language Agents for Ad Hoc Teamwork in the Avalon Game [25.823665278297057]
  This 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.
  arXiv  Detail & Related papers  (2023-12-29T08:26:54Z)
• ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents [77.34720446306419]
  Alympics is a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research. Alympics creates a versatile platform for studying complex game theory problems.
  arXiv  Detail & Related papers  (2023-11-06T16:03:46Z)
• Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models [105.39236338147715]
  The paper is inspired by the popular language game Who is Spy'' We develop DEEP to evaluate LLMs' expression and disguising abilities. We then introduce SpyGame, an interactive multi-agent framework.
  arXiv  Detail & Related papers  (2023-10-31T14:37:42Z)
• LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay [55.12945794835791]
  Using Avalon as a testbed, we employ system prompts to guide LLM agents in gameplay. We propose a novel framework, tailored for Avalon, features a multi-agent system facilitating efficient communication and interaction. Results affirm the framework's effectiveness in creating adaptive agents and suggest LLM-based agents' potential in navigating dynamic social interactions.
  arXiv  Detail & Related papers  (2023-10-23T14:35:26Z)
• Theory of Mind for Multi-Agent Collaboration via Large Language Models [5.2767999863286645]
  This study evaluates Large Language Models (LLMs)-based agents in a multi-agent cooperative text game with Theory of Mind (ToM) inference tasks. We observed evidence of emergent collaborative behaviors and high-order Theory of Mind capabilities among LLM-based agents.
  arXiv  Detail & Related papers  (2023-10-16T07:51:19Z)
• LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models [23.092480882456048]
  This study aims at a detailed analysis of Large Language Models (LLMs) within the context of Pure Coordination Games. Our findings indicate that LLM agents equipped with GPT-4-turbo achieve comparable performance to state-of-the-art reinforcement learning methods. Results on Coordination QA show a large room for improvement in the Theory of Mind reasoning and joint planning abilities of LLMs.
  arXiv  Detail & Related papers  (2023-10-05T21:18:15Z)
• Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation [52.930183136111864]
  We propose using scorable negotiation to evaluate Large Language Models (LLMs) To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities. We provide procedures to create new games and increase games' difficulty to have an evolving benchmark.
  arXiv  Detail & Related papers  (2023-09-29T13:33:06Z)
• Building Cooperative Embodied Agents Modularly with Large Language Models [104.57849816689559]
  We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments. We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework. Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
  arXiv  Detail & Related papers  (2023-07-05T17:59:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.