Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games

• URL: http://arxiv.org/abs/2312.00746v2
• Date: Thu, 29 Feb 2024 06:24:28 GMT
• Title: Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games
• Authors: Dekun Wu, Haochen Shi, Zhiyuan Sun, Bang Liu
• Abstract summary: We introduce the first dataset specifically for Jubensha, including character scripts and game rules. Our work also presents a unique multi-agent interaction framework using LLMs, allowing AI agents to autonomously engage in this game. To evaluate the gaming performance of these AI agents, we developed novel methods measuring their mastery of case information and reasoning skills.
• Score: 26.07074182316433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we explore the application of Large Language Models (LLMs) in \textit{Jubensha}, a Chinese detective role-playing game and a novel area in Artificial Intelligence (AI) driven gaming. We introduce the first dataset specifically for Jubensha, including character scripts and game rules, to foster AI agent development in this complex narrative environment. Our work also presents a unique multi-agent interaction framework using LLMs, allowing AI agents to autonomously engage in this game. To evaluate the gaming performance of these AI agents, we developed novel methods measuring their mastery of case information and reasoning skills. Furthermore, we incorporated the latest advancements in in-context learning to improve the agents' performance in information gathering, murderer identification, and logical reasoning. The experimental results validate the effectiveness of our proposed methods. This work aims to offer a novel perspective on understanding LLM capabilities and establish a new benchmark for evaluating large language model-based agents.

Related papers

• Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization [56.674356045200696]
  We propose a novel method to train AI agents to incorporate knowledge and skills for multiple tasks without the need for cumbersome note systems or prior high-quality demonstration data. Our approach employs an iterative process where the agent collects new experiences, receives corrective feedback from humans in the form of hints, and integrates this feedback into its weights. We demonstrate the efficacy of our approach by implementing it in a Llama-3-based agent which, after only a few rounds of feedback, outperforms advanced models GPT-4o and DeepSeek-V3 in a taskset.
  arXiv  Detail & Related papers  (2025-02-03T17:45:46Z)
• Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information [36.11862095329315]
  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.
  arXiv  Detail & Related papers  (2024-08-05T15:36:46Z)
• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• A Survey on Large Language Model-Based Game Agents [9.892954815419452]
  The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI) This paper provides a comprehensive overview of LLM-based game agents from a holistic viewpoint.
  arXiv  Detail & Related papers  (2024-04-02T15:34:18Z)
• Characteristic AI Agents via Large Language Models [40.10858767752735]
  This research focuses on investigating the performance of Large Language Models in constructing characteristic AI agents. A dataset called Character100'' is built for this benchmark, comprising the most-visited people on Wikipedia for language models to role-play. The experimental results underscore the potential directions for further improvement in the capabilities of LLMs in constructing characteristic AI agents.
  arXiv  Detail & Related papers  (2024-03-19T02:25:29Z)
• 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)
• An In-depth Survey of Large Language Model-based Artificial Intelligence Agents [11.774961923192478]
  We have explored the core differences and characteristics between LLM-based AI agents and traditional AI agents. We conducted an in-depth analysis of the key components of AI agents, including planning, memory, and tool use.
  arXiv  Detail & Related papers  (2023-09-23T11:25:45Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• Tachikuma: Understading Complex Interactions with Multi-Character and Novel Objects by Large Language Models [67.20964015591262]
  We introduce a benchmark named Tachikuma, comprising a Multiple character and novel Object based interaction Estimation task and a supporting dataset. The dataset captures log data from real-time communications during gameplay, providing diverse, grounded, and complex interactions for further explorations. We present a simple prompting baseline and evaluate its performance, demonstrating its effectiveness in enhancing interaction understanding.
  arXiv  Detail & Related papers  (2023-07-24T07:40:59Z)
• Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games [64.11746320061965]
  We study reinforcement learning for text-based games, which are interactive simulations in the context of natural language. We aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents.
  arXiv  Detail & Related papers  (2020-10-22T12:40:22Z)

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.