Flip-Team：带随机人工干预的协作接管游戏
$\texttt{Flip-Team}$: Cooperative Takeover Games with Stochastic Human Override
AI总结：
本文提出带随机人工干预的协作博弈论框架，将共享自主系统的控制切换问题建模为利益一致的动态博弈，推导线性二次系统的最优切换策略闭式递推式，验证了其在不同系统下的有效性，揭示人类适应性与自主效率的权衡。
AI中文摘要：
共享自主系统需要合理的机制来在人类与自主智能体之间分配和转移控制权。现有方法通常依赖于混合控制输入或启发式切换规则，这些方法缺乏理论保证，且无法考虑权限转移的动态性。本文开发了一种用于共享自主系统中权限切换的协作博弈论框架。我们将控制切换问题表述为一种利益一致的动态博弈，其中权限转移被嵌入到系统动态中，从而产生最优切换策略而非临时规则。我们在随机人工干预下，考虑人类保留干预能力的非对称权限情况，建立了纯策略下团队最优策略的存在性及其特征。对于线性二次系统，我们推导了最优切换策略和价值函数的闭式递推式，能够实现与连续状态无关的高效计算。我们在标量和多维线性系统上验证了该框架，展示了最优切换如何适应不同的系统动态、成本结构和干预概率。结果揭示了人类适应性与自主效率之间的基本权衡，说明了将共享自主系统建立在协作博弈论基础上的实际益处。
英文摘要：
Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a cooperative game-theoretic framework for authority switching in shared autonomy. We formulate the control switching problem as an identical-interest dynamic game in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We establish the existence and characterization of team-optimal policies in pure strategies under stochastic human override, accounting for asymmetric authority where humans retain override capability. For linear-quadratic systems, we derive closed-form recursions for the optimal switching policies and value functions, enabling efficient computation independent of the continuous state. We validate the framework on scalar and multi-dimensional linear systems, demonstrating how optimal switching adapts to varying system dynamics, cost structures, and override probabilities. The results reveal fundamental trade-offs between human adaptability and autonomous efficiency, illustrating the practical benefits of grounding shared autonomy in cooperative game theory.
