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arXiv 2512.00513cs.GTcs.MA
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通过近似VCG机制下的即时惩罚实现可信的物联网AI代理

Truthful and Trustworthy IoT AI Agents via Immediate-Penalty Enforcement under Approximate VCG Mechanisms

Toyohashi University of Technology(丰桥技术科学大学);University of Electro-Communications(电波大学);Tohoku University(东北大学);Muroran Institute of Technology(室兰工业大学)

Xun Shao, Ryuuto Shimizu, Zhi Liu, Kaoru Ota, Mianxiong Dong

AI总结:

本文提出了一种结合近似Vickrey-Clarke-Groves双拍卖和即时单次惩罚的物联网能源交易信任保障框架,旨在通过单轮过程恢复 truthful 报告,即使在分配精度近似和监控噪声的情况下,同时通过理论分析和实验验证了轻量级惩罚设计在对齐战略物联网代理与社会最优能源交易结果中的有效性。

摘要:

在物联网能源系统中部署自主AI代理需要在实时约束和不完美监控下保持鲁棒、高效和可信的决策机制。虽然强化学习能够使分布式生产者消费者行为适应性增强,但确保经济一致性和防止战略操纵仍面临挑战,特别是在传感噪声或部分可观测性降低操作员验证行动能力时。本文提出了一种物联网能源交易的信任保障框架,结合了近似Vickrey-Clarke-Groves(VCG)双拍卖与即时单次惩罚。与基于声誉或历史的方法不同,所提机制能够在单轮过程中恢复 truthful 报告,即使在分配精度近似和监控噪声的情况下。我们理论上刻画了近似带来的激励差距,并推导出一个保证在有界传感误差下 truthful 投标成立的惩罚阈值。为评估具备学习能力的生产者消费者,我们将机制嵌入一个多智能体强化学习环境中,以反映随机发电、动态负载和异质交易机会。实验表明,改进的分配精度减少了偏差激励,所需的惩罚匹配分析预测,且即使在不完美监控下,学习到的投标行为仍保持稳定和可解释。这些结果表明,轻量级惩罚设计可以可靠地将战略物联网代理与社会最优能源交易结果对齐。

英文摘要:

The deployment of autonomous AI agents in Internet of Things (IoT) energy systems requires decision-making mechanisms that remain robust, efficient, and trustworthy under real-time constraints and imperfect monitoring. While reinforcement learning enables adaptive prosumer behaviors, ensuring economic consistency and preventing strategic manipulation remain open challenges, particularly when sensing noise or partial observability reduces the operator's ability to verify actions. This paper introduces a trust-enforcement framework for IoT energy trading that combines an approximate Vickrey-Clarke-Groves (VCG) double auction with an immediate one-shot penalty. Unlike reputation- or history-based approaches, the proposed mechanism restores truthful reporting within a single round, even when allocation accuracy is approximate and monitoring is noisy. We theoretically characterize the incentive gap induced by approximation and derive a penalty threshold that guarantees truthful bidding under bounded sensing errors. To evaluate learning-enabled prosumers, we embed the mechanism into a multi-agent reinforcement learning environment reflecting stochastic generation, dynamic loads, and heterogeneous trading opportunities. Experiments show that improved allocation accuracy reduces deviation incentives, the required penalty matches analytical predictions, and learned bidding behaviors remain stable and interpretable despite imperfect monitoring. These results demonstrate that lightweight penalty designs can reliably align strategic IoT agents with socially efficient energy-trading outcomes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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