Autonomous robots that encounter other autonomous agents, be they other robots or people, need to infer the activity/intent of those agents to determine what sorts of behaviors should be invoked. In this project, the term “adversarial” refers to any intent that is not automatically assumed to be explicitly and deliberatively cooperative. Behaviors that the robot may need to invoke include explicitly evading a collision with another vehicle, trying to plan a path around a vehicle that is actively blocking its path, or even to strike up a conversation with an otherwise indifferent person in the hopes of engaging them in cooperative behavior. We are interested in extending the work on passive activity recognition, whereby the intent/activity of the observed agent is inferred through analysis of the observation history, by having the autonomous robot explicitly execute actions/behaviors that will affect the behavior of the observed agent. The selection of specific behaviors in this fashion is a form of automatic hypothesis testing which will allow the robot to more quickly evaluate the states and predicted goals of the agents in its vicinity.