ORCH: Organizational Principles Enable Collective Intelligence in Embodied AI
作者: Zhengran Ji, Jonathan Hyun, Boyuan Chen
分类: cs.MA, cs.AI, cs.LG, cs.RO
发布日期: 2026-09-10
💡 一句话要点
提出ORCH以优化多智能体系统的组织与协调
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 多智能体系统 集体智能 组织理论 任务协调 层级组织 执行效率 野火响应 人工智能
📋 核心要点
- 现有的多智能体系统通常使用固定的组织结构，无法适应不同任务的协调需求，导致效率低下。
- 本文提出ORCH，通过结合并行和顺序依赖，构建任务特定的层级组织，以优化多智能体的协作。
- 实验结果表明，ORCH组织在多个任务中显著提高了执行效率和任务成功率，展示了其优越性。
📝 摘要（中文）
集体智能不仅依赖于个体成员的能力，还取决于成员的组织方式。然而，现有的人工多智能体系统通常采用固定的组织结构，无法满足不同任务的协调需求。本文提出了ORCH（组织角色与协调层级），通过结合并行依赖和顺序依赖构建任务特定的层级组织。在25个野火响应任务中，使用多达50个异构智能体的团队进行了评估，结果显示，基于ORCH构建的组织在任务结果、执行效率、探索能力和计算资源使用上均优于四种代表性的多智能体方法。人类设计的ORCH组织平均提高了63.97%的最终得分和74.29%的执行效率，而自动生成的组织也实现了43.63%和52.53%的提升。这些优势在不同任务和语言模型中均得以保持。
🔬 方法详解
问题定义：本文旨在解决现有多智能体系统在组织结构上的局限性，尤其是在面对不同协调需求的任务时，固定结构导致的效率低下问题。
核心思路：ORCH通过引入人类组织理论的原则，结合并行依赖与顺序依赖，构建适应性强的层级组织，从而提升多智能体的协作能力。
技术框架：ORCH的整体架构包括任务分析、角色分配和协调机制三个主要模块。首先，分析任务需求以确定并行和顺序依赖关系；然后，根据分析结果分配角色；最后，实施协调机制以优化任务执行。
关键创新：ORCH的核心创新在于其灵活的组织结构设计，能够根据具体任务动态调整，而不是依赖于固定的组织形式。这一设计使得多智能体能够更高效地协作。
关键设计：在实现过程中，ORCH采用了特定的参数设置以优化角色分配，并设计了适应性强的损失函数来评估协作效果。此外，网络结构上结合了多种语言模型，以增强组织的智能化水平。
🖼️ 关键图片
📊 实验亮点
实验结果显示，基于ORCH构建的组织在25个野火响应任务中，平均提高了63.97%的最终得分和74.29%的执行效率。自动生成的组织也实现了43.63%和52.53%的提升，展现了其在多种任务中的稳定优势。
🎯 应用场景
该研究的潜在应用领域包括灾害响应、物流管理和复杂系统的协调等。通过优化多智能体的组织与协调，ORCH能够显著提升团队在动态环境中的执行效率和任务成功率，具有重要的实际价值和广泛的应用前景。
📄 摘要（原文）
Collective intelligence depends not only on the capabilities of individual members, but also on how those members are organized. Yet artificial multi-agent systems are typically assembled using fixed organizational structures, even when the physical tasks they perform impose fundamentally different coordination requirements. Here we show that principles from human organization theory can be operationalized to organize large, heterogeneous collectives of embodied artificial agents. We introduce ORCH (Organizing Roles and Coordination Hierarchies), which constructs task-specific hierarchical organizations by combining pooled interdependence for work that can proceed concurrently with sequential interdependence for work governed by prerequisite relationships. Across 25 wildfire-response missions spanning reconnaissance, rescue, transportation, resource management, containment and suppression, we evaluated teams of up to 50 heterogeneous agents using eight large language models. Organizations constructed using these principles consistently outperformed four representative embodied multi-agent approaches across mission outcome, execution efficiency, exploration and computational resource use. Human-designed ORCH organizations improved final score by 63.97% and execution efficiency by 74.29% on average relative to the four prior frameworks. Organizations generated automatically by language models improved these measures by 43.63% and 52.53%, respectively. These advantages persisted across missions and underlying language models. Notably, collective performance was not monotonically determined by model scale. Analysis of long-horizon missions showed that hierarchical organization enabled teams to preserve concurrent activity within specialized groups while coordinating ordered transitions between mission phases.