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 in repeated trials. 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.

中文翻译：

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多智能体交互的领导力推断

有效预测意图和行为需要推断多智能体交互中的领导力。动态博弈为这些交互的建模提供了一个富有表现力的理论框架。利用这个框架，我们提出了一种新颖的方法，通过观察智能体在复杂的长视野交互中的行为来推断二智能体博弈中的领导者。我们做出了两项贡献。首先，我们介绍一种解决动态二代理 Stackelberg 博弈的迭代算法具有非线性动力学和非二次成本，并证明它在重复试验中始终收敛。其次，我们提出了 Stackelberg Leadership Filter (SLF)，这是一种基于对游戏交互的观察来识别交互场景中的领先智能体的在线方法。我们验证了领导力过滤器在模拟驾驶场景中的功效，以证明 SLF 可以得出与通行权期望相匹配的领导力结论。