  Autonomous agents should be able to coordinate with other agents without
knowing their intents ahead of time. While prior work has studied how agents
can gather information about the intent of others, in this work we study the
inverse problem: how agents can demonstrate their intent to others, within the
framework of general-sum dynamic games. We first present a model of this intent
demonstration problem and then propose an algorithm that enables an agent to
trade off their task performance and intent demonstration to improve the
overall system's performance. To scale to continuous states and action spaces
as well as to nonlinear dynamics and costs, our algorithm leverages
linear-quadratic approximations with an efficient intent teaching guarantee.
Our empirical results show that intent demonstration accelerates other agents'
learning and enables the demonstrating agent to balance task performance with
intent expression.
