IndoorWorld：在异构多智能体环境中整合物理任务求解与社会模拟
IndoorWorld: Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment
AI总结：
该研究提出整合物理与社会动态的异构多智能体环境IndoorWorld，解决现有LLM智能体环境两类研究割裂的问题，通过办公场景实验验证其在建筑居住者模拟等方向的应用潜力。
摘要：
虚拟环境对AI智能体研究至关重要。现有面向大语言模型（LLM）智能体研究的环境通常要么聚焦于物理任务求解，要么聚焦于社会模拟——前者过度简化了智能体的个体性与社会动态，后者则缺乏社会行为的物理基础。我们提出IndoorWorld，这是一个紧密整合物理与社会动态的异构多智能体环境。通过为LLM驱动的智能体引入新挑战：协调社会动态以影响物理环境，以及将社会交互锚定在世界状态中，IndoorWorld为建筑设计领域基于LLM的建筑居住者模拟开辟了可能。我们在办公场景中开展了一系列实验，检验多智能体协作、资源竞争与空间布局对智能体行为的影响，以此证明其潜力。
英文摘要：
Virtual environments are essential to AI agent research. Existing environments for LLM agent research typically focus on either physical task solving or social simulation, with the former oversimplifying agent individuality and social dynamics, and the latter lacking physical grounding of social behaviors. We introduce IndoorWorld, a heterogeneous multi-agent environment that tightly integrates physical and social dynamics. By introducing novel challenges for LLM-driven agents in orchestrating social dynamics to influence physical environments and anchoring social interactions within world states, IndoorWorld opens up possibilities of LLM-based building occupant simulation for architectural design. We demonstrate the potential with a series of experiments within an office setting to examine the impact of multi-agent collaboration, resource competition, and spatial layout on agent behavior.
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