用上下文触发的博弈机制，让多个智能体在动态环境中安全自适应协作。
Context-Triggered Contingency Games for Strategic Multi-Agent Interaction
- 分层架构：策略模板保目标，因子图求解器实现实时控制
- 仿真与硬件实验验证，在自动驾驶和导航中实现高效可靠交互
- 适合需要安全协同的多智能体系统，如自动驾驶车队
我们解决自主多智能体系统中可靠高效交互的挑战，要求智能体在长期战略目标与短期动态适应之间取得平衡。提出上下文触发的应急博弈，将基于时序逻辑规范的战略博弈与实时求解的动态应急博弈相融合。采用双层架构，利用策略模板保证高层目标满足，引入新的基于因子图的求解器，实现可扩展的实时模型预测控制。该框架在不确定、交互性强的环境中确保安全与进展。通过自动驾驶和机器人导航的仿真与硬件实验验证，展示了高效、可靠且自适应的多智能体交互能力。
原文摘要 · Abstract (English)
We address the challenge of reliable and efficient interaction in autonomous multi-agent systems, where agents must balance long-term strategic objectives with short-term dynamic adaptation. We propose context-triggered contingency games, a novel integration of strategic games derived from temporal logic specifications with dynamic contingency games solved in real time. Our two-layered architecture leverages strategy templates to guarantee satisfaction of high-level objectives, while a new factor-graph-based solver enables scalable, real-time model predictive control of dynamic interactions. The resulting framework ensures both safety and progress in uncertain, interactive environments. We validate our approach through simulations and hardware experiments in autonomous driving and robotic navigation, demonstrating efficient, reliable, and adaptive multi-agent interaction.
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