In many settings where multiple agents interact, the optimal choices for each
agent depend heavily on the choices of the others. These coupled interactions
are well-described by a general-sum differential game, in which players have
differing objectives, the state evolves in continuous time, and optimal play
may be characterized by one of many equilibrium concepts, e.g., a Nash
equilibrium. Often, problems admit multiple equilibria. From the perspective of
a single agent in such a game, this multiplicity of solutions can introduce
uncertainty about how other agents will behave. This paper proposes a general
framework for resolving ambiguity between equilibria by reasoning about the
equilibrium other agents are aiming for. We demonstrate this framework in
simulations of a multi-player human-robot navigation problem that yields two
main conclusions: First, by inferring which equilibrium humans are operating
at, the robot is able to predict trajectories more accurately, and second, by
discovering and aligning itself to this equilibrium the robot is able to reduce
the cost for all players.

文章提出了一个通用框架，通过推断其他代理方的平衡状态来解决不确定性问题，并在多人机器人导航问题的模拟中证明，通过对准平衡状态，机器人可以更准确地预测轨迹并降低所有玩家的成本。