基于博弈论视角的AI对齐：一项综述
AI Alignment through a Game-theoretic Lens: A Survey
- James Cook University（詹姆斯库克大学）
- Western Sydney University（西悉尼大学）
- Adelaide University（阿德莱德大学）
- CSIRO（联邦科学与工业研究组织）
- The University of Osaka（大阪大学）
- Nanjing University of Science and Technology（南京理工大学）
- Macquarie University（麦考瑞大学）
- La Trobe University（拉筹伯大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本综述从博弈论视角梳理AI对齐的近期进展，针对偏好多样性等三大挑战整合相关文献，明确了博弈论在AI对齐中的作用、不足及未来构建稳健AI系统的挑战。
中文摘要 AI 辅助
随着大型语言模型和能力日益增强的AI智能体被部署到高风险场景中，使其与复杂的人类价值观对齐已成为核心挑战。现有的对齐方法虽能有效提升有用性、无害性和可控性，但往往难以捕捉依赖于上下文、非传递性且受多方动态互动影响的现实偏好。本综述从博弈论视角审视AI对齐，具体围绕关键博弈论要素梳理近期进展，并从偏好多样性、对齐优先级和时间动态性这三个挑战维度整合相关文献。该视角明确了现有对齐方法在哪些方面真正得益于博弈论分析、该框架在哪些方面较为松散，以及在构建稳健、自适应且可验证的AI系统方面仍存在哪些挑战。
英文摘要
As large language models and increasingly capable AI agents are deployed in high-risk settings, aligning them with complex human values has become a central challenge. Existing alignment methods, while effective in improving helpfulness, harmlessness, and controllability, often struggle to capture real-world preferences that are context-dependent, non-transitive, and shaped by dynamic multi-party interactions. This survey reviews AI alignment through a game-theoretic lens. Specifically, it organizes recent progress around key game-theoretic elements and synthesizes the literature along three challenges: preference diversity, alignment priority, and temporal dynamics. This perspective clarifies where current alignment methods genuinely benefit from game-theoretic analysis, where the framework is looser, and what challenges remain in building robust, adaptive, and verifiable AI systems.
