Digital Player: Evaluating Large Language Models based Human-like Agent in Games

• URL: http://arxiv.org/abs/2502.20807v1
• Date: Fri, 28 Feb 2025 07:46:55 GMT
• Title: Digital Player: Evaluating Large Language Models based Human-like Agent in Games
• Authors: Jiawei Wang, Kai Wang, Shaojie Lin, Runze Wu, Bihan Xu, Lingeng Jiang, Shiwei Zhao, Renyu Zhu, Haoyu Liu, Zhipeng Hu, Zhong Fan, Le Li, Tangjie Lyu, Changjie Fan,
• Abstract summary: We develop an application-level testbed based on the open-source strategy game "Unciv"<n>This "Civilization"-like game features expansive decision-making spaces along with rich linguistic interactions.<n>We generate human-like responses for social interaction, collaboration, and negotiation with human players.
• Score: 35.28605831046303
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: With the rapid advancement of Large Language Models (LLMs), LLM-based autonomous agents have shown the potential to function as digital employees, such as digital analysts, teachers, and programmers. In this paper, we develop an application-level testbed based on the open-source strategy game "Unciv", which has millions of active players, to enable researchers to build a "data flywheel" for studying human-like agents in the "digital players" task. This "Civilization"-like game features expansive decision-making spaces along with rich linguistic interactions such as diplomatic negotiations and acts of deception, posing significant challenges for LLM-based agents in terms of numerical reasoning and long-term planning. Another challenge for "digital players" is to generate human-like responses for social interaction, collaboration, and negotiation with human players. The open-source project can be found at https:/github.com/fuxiAIlab/CivAgent.

Related papers

• Game Development as Human-LLM Interaction [55.03293214439741]
  This paper introduces the Chat Game Engine (ChatGE) powered by Human-LLM interaction. ChatGE allows everyone to develop a custom game using natural language through Human-LLM interaction. We construct a ChatGE for poker games as a case study and evaluate it from two perspectives: interaction quality and code correctness.
  arXiv  Detail & Related papers  (2024-08-18T07:06:57Z)
• Exploring Autonomous Agents through the Lens of Large Language Models: A Review [0.0]
  Large Language Models (LLMs) are transforming artificial intelligence, enabling autonomous agents to perform diverse tasks across various domains. They face challenges such as multimodality, human value alignment, hallucinations, and evaluation. Evaluation platforms like AgentBench, WebArena, and ToolLLM provide robust methods for assessing these agents in complex scenarios.
  arXiv  Detail & Related papers  (2024-04-05T22:59:02Z)
• 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)
• CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents [63.79739920174535]
  We introduce CivRealm, an environment inspired by the Civilization game. CivRealm stands as a unique learning and reasoning challenge for decision-making agents.
  arXiv  Detail & Related papers  (2024-01-19T09:14:11Z)
• SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems [53.94772445896213]
  Large Language Model (LLM)-based multi-agent systems have demonstrated promising performance in simulating human society. We propose SpeechAgents, a multi-modal LLM based multi-agent system designed for simulating human communication.
  arXiv  Detail & Related papers  (2024-01-08T15:01:08Z)
• Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games [26.07074182316433]
  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.
  arXiv  Detail & Related papers  (2023-12-01T17:33:57Z)
• Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models [6.176709034158014]
  We explore the game of Avalon: The Resistance, a social deduction game in which players must determine each other's hidden identities to complete their team's objective. We introduce an online testbed and a dataset containing 20 carefully collected and labeled games that exhibit long-horizon deception in a cooperative-competitive setting. We discuss the capabilities of LLMs to utilize deceptive long-horizon conversations between six human players to determine each player's goal and motivation.
  arXiv  Detail & Related papers  (2023-11-09T20:04:08Z)
• 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)
• Agents: An Open-source Framework for Autonomous Language Agents [98.91085725608917]
  We consider language agents as a promising direction towards artificial general intelligence. We release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience.
  arXiv  Detail & Related papers  (2023-09-14T17:18:25Z)
• 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)
• Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation [24.05034588588407]
  This paper addresses a key aspect in the design of such agents: Predicting human decision in off-policy evaluation. We collected a dataset of 87K decisions from humans playing a repeated decision-making game with artificial agents. Our approach involves training a model on human interactions with one agents subset to predict decisions when interacting with another.
  arXiv  Detail & Related papers  (2023-05-17T16:38:11Z)

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.