Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models. Mobile AIGC services in the Internet of Vehicles (IoV) network have numerous advantages over traditional cloud-based AIGC services, including enhanced network efficiency, better reconfigurability, and stronger data security and privacy. Nonetheless, AIGC service provisioning frequently demands significant resources. Consequently, resource-constrained roadside units (RSUs) face challenges in maintaining a heterogeneous pool of AIGC services and addressing all user service requests without degrading overall performance. Therefore, in this paper, we propose a decentralized incentive mechanism for mobile AIGC service allocation, employing multi-agent deep reinforcement learning to find the balance between the supply of AIGC services on RSUs and user demand for services within the IoV context, optimizing user experience and minimizing transmission latency. Experimental results demonstrate that our approach achieves superior performance compared to other baseline models.

翻译：人工智能生成内容（AIGC）是指利用AI模型自动生成内容的范式。与传统云端AIGC服务相比，车联网（IoV）中的移动AIGC服务具有显著优势，包括提升网络效率、增强可重构性以及更强的数据安全与隐私保护能力。然而，AIGC服务的提供通常需要大量资源。因此，资源受限的路侧单元（RSUs）在维护异构AIGC服务池的同时，难以在不降低整体性能的情况下满足所有用户服务请求。为此，本文提出一种面向移动AIGC服务分配的去中心化激励机制，采用多智能体深度强化学习在车联网背景下平衡RSUs上的AIGC服务供给与用户服务需求，从而优化用户体验并最小化传输延迟。实验结果表明，与基线模型相比，本方法取得了更优的性能。