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.

翻译：我们解决了自主多智能体系统中可靠且高效交互的挑战，其中智能体需平衡长期策略目标与短期动态适应。我们提出上下文触发的应急博弈，将源于时序逻辑规范的策略博弈与实时求解的动态应急博弈进行创新融合。该双层架构利用策略模板保证高层级目标的满足，同时基于新型因子图的求解器能够实现可扩展的实时模型预测控制动态交互。最终框架确保了不确定性交互环境中的安全性与进展性。我们通过自动驾驶与机器人导航的仿真及硬件实验验证了该方法，展示了高效、可靠且自适应的多智能体交互能力。