Effectively predicting intent and behavior requires inferring leadership in multi-agent interactions. Dynamic games provide an expressive theoretical framework for modeling these interactions. Employing this framework, we propose a novel method to infer the leader in a two-agent game by observing the agents' behavior in complex, long-horizon interactions. We make two contributions. First, we introduce an iterative algorithm that solves dynamic two-agent Stackelberg games with nonlinear dynamics and nonquadratic costs, and demonstrate that it consistently converges. Second, we propose the Stackelberg Leadership Filter (SLF), an online method for identifying the leading agent in interactive scenarios based on observations of the game interactions. We validate the leadership filter's efficacy on simulated driving scenarios to demonstrate that the SLF can draw conclusions about leadership that match right-of-way expectations.

翻译：有效预测意图和行为需要推断多智能体交互中的领导力。动态博弈为建模这类交互提供了具有表达力的理论框架。基于该框架，我们提出一种新颖方法，通过观测复杂、长时域交互中智能体的行为来推断双智能体博弈中的领导者。本文做出两项贡献：首先，我们引入一种迭代算法，用于求解具有非线性动力学和非二次成本的动态双智能体斯塔克尔伯格博弈，并证明该算法能够一致收敛；其次，我们提出斯塔克尔伯格领导力滤波器，一种基于博弈交互观测在线识别交互场景中主导智能体的方法。通过在模拟驾驶场景中验证该领导力滤波器的有效性，证明SLF能够得出与通行权期望相符的领导力结论。