用大模型模拟虚拟人性格，让应急疏散更真实
LLM-Driven Personalities for Decision Making in Emergency Simulations
- 用大模型根据性格特质生成决策行为
- 不同性格导致明显不同的疏散行为模式
- 适合想提升仿真真实性的研究人员
为了让虚拟人表现得可信，需具备自主性与空间意识，并以体现能力与智慧的方式与环境互动。核心在于有效的决策机制。随着人工智能快速发展，大语言模型（LLMs）被探索用于支持此类决策过程。本文研究在模拟疏散场景中，利用LLM驱动虚拟人的决策，将OCEAN人格特质融入代理表征。目标是评估通过语言提示表达的人格如何影响个体行为及整体仿真结果。结果显示，基于LLM的人格配置显著影响代理决策，形成不同特质下的独特行为模式。这表明，由LLM引导的异质人群可增强仿真环境的真实性和多样性，为传统规则基方法提供灵活替代方案。
原文摘要 · Abstract (English)
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.
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