用于紧急模拟决策的LLM驱动个性
LLM-Driven Personalities for Decision Making in Emergency Simulations
AI总结：
本研究利用大语言模型（LLM）结合OCEAN人格特质驱动虚拟人在疏散模拟中的决策，发现不同人格特质显著影响个体行为和群体模拟结果，增强了模拟环境的真实性和多样性。
摘要：
为了让虚拟人显得可信，它们必须在与环境交互时表现出能动性和空间意识，以体现能力和智能。这些能力的核心在于有效的决策，它强烈塑造了智能体的行为。随着人工智能的快速发展，大语言模型（LLM）越来越多地被探索作为支持此类决策过程的机制。在这项工作中，我们研究了在模拟疏散场景中使用LLM驱动虚拟人的决策，将OCEAN人格特质融入智能体表征中。我们的目标是评估通过语言提示表达的人格如何影响个体行为和集体模拟结果。我们的结果表明，LLM驱动的人格特征显著影响智能体的决策，导致不同特质下的不同行为模式。这些发现表明，由LLM引导的智能体组成的异质人群可以增强模拟环境的真实性和可变性，为传统的基于规则的方法提供了一种灵活的替代方案。
英文摘要：
For virtual humans to appear believable, they must exhibit agency and spatial awareness while interacting with their environment in ways that reflect competence and intelligence. At the core of these capabilities lies effective decision-making, which strongly shapes agent behavior. With the rapid advancement of artificial intelligence, Large Language Models (LLMs) have increasingly been explored as a mechanism to support such decision-making processes. In this work, we investigate the use of LLMs to drive decision-making in virtual humans within a simulated evacuation scenario, incorporating OCEAN personality traits into agent representations. Our goal is to evaluate how personality, expressed through language-based prompts, influences both individual behaviors and collective simulation outcomes. Our results demonstrate that LLM-driven personality profiles significantly impact agents' decisions, leading to distinct behavioral patterns across different traits. These findings suggest that heterogeneous crowds composed of LLM-guided agents can enhance the realism and variability of simulated environments, offering a flexible alternative to traditional rule-based approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
