外观
论文arXiv 2609.229671 个月前
通用协作智能：面向韧性多智能体生态系统的认知架构设计
原文链接：General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems（arXiv 2609.22967 · 发布日期：2026-09-17）
General Collaborative Intelligence: Architecting Cognition for Resilient Multi-Agent Ecosystems
针对现有综述割裂问题，本文用五维分类和三项认知协同条件统一梳理多智能体无人系统协作智能。
核心要点
- 多智能体无人系统需协作应对遮挡、传感范围有限和环境退化等局部观测陷阱。
- 以五维分类和三项认知协同条件，统一分析协作智能各维度。
- 综述协作架构、神经通信协同设计、多智能体强化学习和韧性机制。
- 提出GCI-Bench五支柱评分协议，并反思可复现性与Sim-to-Real鸿沟。
英文摘要 (Abstract)
Multi-agent unmanned systems are moving from isolated, ego-centric sensing toward collaborative intelligence, in which distributed agents exchange compact features to overcome a local observation trap that no single agent can escape: occlusions, finite sensor range, and environmental degradation. The field has matured across architectural, communication, embodied, resilience, and trust dimensions, yet existing surveys examine these dimensions in isolation and rarely expose their dependencies. This review offers a unified synthesis through two complementary lenses. The first is a five-dimensional taxonomy spanning collaboration stage, communication paradigm, fusion architecture, learning strategy, and application domain. The second is three cognitive synergy conditions, Semantic Disambiguation, Pragmatic Information Exchange, and Proactive Informational Foraging, that turn cognitive synergy into operational criteria. Across these lenses we survey collaboration architectures and topologies, neural-communication co-design that treats the channel as a differentiable pipeline component, embodied action-perception loops via multi-agent reinforcement learning, and resilience mechanisms for synchronization, uncertainty quantification, and label-efficient learning. We then map these advances onto four operational domains, V2X, unmanned aerial, industrial logistics, and smart cities, and onto the safety-privacy-utility triad. To counter benchmark saturation and evaluation fragmentation, we propose GCI-Bench, a five-pillar scoring protocol with a maturity model that makes the trade-offs of collaborative methods comparable across studies. A critical reflection on reproducibility, the sim-to-real gulf, and conditions under which collaboration degrades performance identifies open challenges and charts directions toward general collaborative intelligence under real-world uncertainty.
作者：Lei Zhang, Chun Ye, Le Yang, Zhaozhong Wang, Deng-Ping Fan, Hang Dai, Binglu Wang
来源
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