语言驱动的多智能体交互中涌现的人群动力学
Emergent Crowds Dynamics from Language-Driven Multi-Agent Interactions
AI总结：
本文提出利用大语言模型结合对话系统与语言驱动导航控制人群智能体移动，使智能体根据感知和对话决策，从而在复杂场景中自然涌现分组等真实群体行为。
摘要：
使用基于智能体的方法对人群进行动画模拟是一个成熟的领域，其中人群中的每个智能体都被单独控制，从而涌现出全局的类人行为。我们观察到，人类在人群中的导航和移动往往受到复杂的社会与环境交互的影响，而这些交互主要由语言和对话驱动。然而，现有大多数工作并未考虑这些维度，导致动画中智能体与智能体之间以及智能体与环境之间的交互在很大程度上仅限于转向控制和固定的高层目标外推。我们提出了一种利用大语言模型（LLMs）控制智能体移动的新方法。该方法包含两个主要组成部分：对话系统和语言驱动导航。我们根据角色性格、角色身份、欲望和关系，周期性地查询以智能体为中心的大语言模型，以便在与邻近智能体的空间和社会关系需要时控制智能体间对话的生成。随后，我们利用对话内容以及每个智能体的性格、情绪状态、视觉和物理状态来控制每个智能体的导航和转向。因此，我们的模型使智能体能够基于其感知输入和正在进行的对话做出运动决策。我们在两个复杂场景中验证了该方法，这两个场景体现了社会交互、转向控制和拥挤之间的相互作用。在这些场景中，我们观察到智能体会自动发生分组和拆组。此外，实验表明，我们的方法充当了人群内部的信息传递机制。因此，我们的框架能够产生更真实的人群模拟，涌现的群体行为可以从任何环境设置中自然产生。
英文摘要：
Animating and simulating crowds using an agent-based approach is a well-established area where every agent in the crowd is individually controlled such that global human-like behaviour emerges. We observe that human navigation and movement in crowds are often influenced by complex social and environmental interactions, driven mainly by language and dialogue. However, most existing work does not consider these dimensions and leads to animations where agent-agent and agent-environment interactions are largely limited to steering and fixed higher-level goal extrapolation. We propose a novel method that exploits large language models (LLMs) to control agents' movement. Our method has two main components: a dialogue system and language-driven navigation. We periodically query agent-centric LLMs conditioned on character personalities, roles, desires, and relationships to control the generation of inter-agent dialogue when necessitated by the spatial and social relationships with neighbouring agents. We then use the conversation and each agent's personality, emotional state, vision, and physical state to control the navigation and steering of each agent. Our model thus enables agents to make motion decisions based on both their perceptual inputs and the ongoing dialogue. We validate our method in two complex scenarios that exemplify the interplay between social interactions, steering, and crowding. In these scenarios, we observe that grouping and ungrouping of agents automatically occur. Additionally, our experiments show that our method serves as an information-passing mechanism within the crowd. As a result, our framework produces more realistic crowd simulations, with emergent group behaviours arising naturally from any environmental setting.
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。
