发表机构
LTCI, Télécom Paris, Institut Polytechnique de Paris; Dassault Aviation; AMIAD; The University of Texas at Austin（巴黎综合理工学院电信学院LTCI实验室; 达索航空公司; AMIAD; 德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多智能体意图异步揭示的应急博弈，提出多分支架构与ADMM并行求解器，在紧密耦合三智能体实验中优于单分支且求解更快。
AI 中文摘要
应急博弈使智能体能够通过构建具有共享前缀和意图相关分支的轨迹，来预测并规划其他智能体的假设意图。虽然应急博弈捕捉了意图不确定性，但现有公式依赖于单一分支时间，过度简化了不同智能体的意图在不同时间被揭示的交互。此外，此类问题的计算成本随智能体和意图数量的增加而迅速增长，因为所有依赖于场景的最佳响应必须联合求解。我们提出了一种多分支应急架构，其中意图不确定性的来源可以在不同的分支时间逐步解决，从而使规划轨迹能够适应意图的异步揭示。我们还开发了一种基于ADMM的求解器，利用场景级并行性。在紧密耦合的三智能体交互上的实验表明，所提出的架构优于传统的单分支架构，同时实现了更低的平均滚动时域求解时间。
英文摘要
Contingency games enable agents to anticipate and plan for other agents' hypothetical intents by constructing trajectories with a shared prefix and intent-dependent branches. While contingency games capture intent uncertainty, existing formulations rely on a single branching time, oversimplifying interactions in which different agents' intentions are revealed at different times. Moreover, the computational cost of such problems grows rapidly with the number of agents and intents, as all scenario-dependent best responses must be solved jointly. We introduce a multi-branch contingency architecture in which sources of intent uncertainty can be resolved progressively at different branching times, allowing the planned trajectories to adapt to the asynchronous revelation of intents. We also develop an ADMM-based solver that exploits scenario-level parallelism. Experiments on tightly coupled three-agent interactions support that the proposed architecture outperforms the conventional single-branch while achieving a lower mean receding-horizon solve time.
