  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.
