为多智能体系统设计自适应控制图，实现自动化监控与安全预警
Control Charts for Multi-agent Systems
- 基于过程理论扩展自适应控制图，支持动态多智能体系统监控
- 实验表明学习型智能体系统需自适应控制图才能有效监测
- 发现缓慢背叛的对抗智能体可规避检测，揭示学习与安全的权衡
生成式智能体已在多种场景中展现出强大辅助能力。随着用户在开放、多智能体环境中部署智能体时限制越来越少，现有对开放型多智能体系统动态的监控方法仍局限于定性观察。本文将过程理论中的自适应控制图拓展至多智能体系统，实现自动化监控。通过仿真验证，自适应控制图对能够从环境学习的多智能体系统至关重要。进一步地，我们从理论和实证两方面证明，自适应控制图易受缓慢背叛的对抗智能体攻击。这一结果揭示了多智能体系统控制中的根本矛盾：系统若允许智能体学习，则必然面临对抗风险。
原文摘要 · Abstract (English)
Generative agents have proven to be powerful assistants in a wide variety of contexts. Given this success, users are now deploying agents with minimal restrictions in open ended, multi-agent environments. Current methods for monitoring the dynamics of open-ended multi-agent systems are limited to qualitative inspection. In this paper, we extend the process-theoretic notion of adaptive control charts to multi-agent systems to enable automated monitoring. Using simulation, we demonstrate that adaptive control charts are necessary for monitoring multi-agent systems that can learn from their environment. We further demonstrate, both empirically and theoretically, that adaptive control charts are susceptible to adversarial agents that defect sufficiently slowly. These results illustrate a fundamental tradeoff in multi-agent system control: either agents in a system cannot learn or the system is susceptible to adversaries.
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