发表机构
North Dakota State University; University of Washington（北达科他州立大学; 华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对开放数字孪生生态中AI智能体的验证需求，提出神经符号去中心化治理框架，结合神经推理与制度治理，通过区块链智能合约实现可审计参与，经原型验证可管控开销下保障合规协作。
AI 中文摘要
受大语言模型赋能的自主AI智能体正逐渐成为数字孪生生态中高风险决策支持人机系统的重要组成部分。然而，现有多智能体系统往往缺乏对身份、能力及政策合规性的可靠验证，尤其是在跨多个机构的去中心化环境中。本文提出一种用于协作数字孪生环境中可验证智能体的神经符号去中心化治理框架。该框架通过多层语义画像表征智能体，将概率性神经推理与确定性制度治理相结合，从而支持可信的人机协作及有意义的人类监督。能力基于形式化领域本体，以实现机器可解释、感知政策且上下文敏感的参与。这些由机构权威方颁发的凭证，通过基于区块链的智能合约进行验证，确保参与可审计且不暴露敏感数据。我们使用包含诊所、数字孪生及可穿戴设备提供商智能体的决策支持原型演示该框架，其可有效防止未授权交互并以可管控的开销执行机构政策。研究结果表明，神经符号去中心化治理为跨机构边界的安全人机协作提供了可扩展且可信的途径。
英文摘要
Autonomous AI agents, increasingly empowered by large language models, are becoming important components of human-machine systems for high-stakes decision support in digital twin ecosystems. However, existing multi-agent systems often lack robust verification for identity, capability, and policy compliance, especially in decentralized environments spanning multiple institutions. This paper proposes a neuro-symbolic decentralized governance framework for verifiable agents in collaborative digital twin environments. By representing agents through multi-layer semantic profiles, the framework bridges probabilistic neural reasoning with deterministic institutional governance, thereby supporting trustworthy human-AI collaboration and meaningful human oversight. Capabilities are grounded in formal domain ontologies to enable machine-interpretable, policy-aware, and context-sensitive participation. These credentials, issued by organizational authorities, are validated via blockchain-based smart contracts, ensuring auditable participation without exposing sensitive data. We demonstrate the framework using a decision-support prototype with clinic, digital twin, and wearable provider agents effectively prevents unauthorized interaction and enforces institutional policies with manageable overhead. Our findings suggest that neuro-symbolic decentralized governance provides a scalable and trustworthy pathway for safe human-machine collaboration across institutional boundaries.
CommentsAccepted at the 2026 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2026), Bellevue, WA, USA, October 4-7, 2026. 7 pages, 1 figure, 5 tables. Code: https://github.com/weicaiuw/verigov-ai (DOI: 10.5281/zenodo.21706699)
