分布式智能体系统：具身智能体间的容错协作
Distributed Agent System: Fault-Tolerant Collaboration Among Embodied Agents
浏览论文内容
中文总结 AI 辅助
针对人工智能工程中智能体驱动任务执行面临的可靠性挑战，提出分布式智能体系统（DAS）框架，重新定义智能体可靠性，构建两层容错架构，为工业场景中异构具身智能体可靠协作提供实用工程途径。
中文摘要 AI 辅助
人工智能工程正从大型语言模型的被动文本生成转向智能体驱动的任务执行，在资源受限和环境不确定的情况下，为长期任务带来了新的可靠性挑战。传统的错误消除优化策略无法解决累积错误传播问题。本文提出了分布式智能体系统（DAS），这是一种用于异构智能体间容错协作的设备-边缘-云框架。我们将智能体可靠性重新定义为系统级容错，而非单轮零错误精度，并提出了一种两层容错架构：通过容错对齐实现单智能体执行可靠性，以及通过半形式语言协议实现跨智能体通信可靠性。该框架为工业场景中可靠的异构具身智能体协作提供了一条实用的工程途径。
英文摘要
AI engineering is shifting from passive text generation by large language models (LLMs) to agent-driven task execution, creating new reliability challenges for long-horizon tasks under resource constraints and environmental uncertainty. Conventional error-elimination optimization strategies fail to address cumulative error propagation. This paper proposes Distributed Agent System (DAS), a device-edge-cloud framework for fault-tolerant collaboration among heterogeneous agents. We redefine agent reliability as system-level fault tolerance rather than single-turn zero-error accuracy, and present a two-layer fault-tolerance architecture: single-agent execution reliability via fault-tolerant alignment, and cross-agent communication reliability via semi-formal language protocols. This framework provides a practical engineering pathway for reliable heterogeneous embodied agents collaboration in industrial scenarios.
发表机构
- Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。
