Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot's actions impact the decisions of other agents and vice versa. The resulting contingency game allows the robot to efficiently interact with other agents by generating strategic motion plans conditioned on multiple possible intents for other actors in the scene. Contingency games are parameterized via a scalar variable which represents a future time when intent uncertainty will be resolved. By estimating this parameter online, we construct a game-theoretic motion planner that adapts to changing beliefs while anticipating future certainty. We show that existing variants of game-theoretic planning under uncertainty are readily obtained as special cases of contingency games. Through a series of simulated autonomous driving scenarios, we demonstrate that contingency games close the gap between certainty-equivalent games that commit to a single hypothesis and non-contingent multi-hypothesis games that do not account for future uncertainty reduction.

翻译：应急规划是指智能体根据不确定事件的结果生成一组可能计划的方法，这种方法在机器人应对不确定性时日益流行。在本研究中，我们从博弈论视角探讨应急规划，专门针对机器人行为影响其他智能体决策、反之亦然的交互场景。由此产生的应急博弈使机器人能够通过生成基于场景中其他参与者多种可能意图的战略性运动规划，高效地与其他智能体交互。应急博弈通过一个标量变量进行参数化，该变量代表意图不确定性得以解决的未来时刻。通过在线估计该参数，我们构建了一种能够适应不断变化信念并预判未来确定性的博弈论运动规划器。研究表明，现有不确定性条件下的博弈论规划变体可以视为应急博弈的特例。通过一系列模拟自动驾驶场景，我们证明应急博弈缩小了承诺单一假设的确定性等价博弈与未考虑未来不确定性的非应急多假设博弈之间的差距。