  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.
