  Many intelligent systems currently interact with others using at least one of
fixed communication inputs or preset responses, resulting in rigid interaction
experiences and extensive efforts developing a variety of scenarios for the
system. Fixed inputs limit the natural behavior of the user in order to
effectively communicate, and preset responses prevent the system from adapting
to the current situation unless it was specifically implemented. Closed-loop
interaction instead focuses on dynamic responses that account for what the user
is currently doing based on interpretations of their perceived activity. Agents
employing closed-loop interaction can also monitor their interactions to ensure
that the user responds as expected. We introduce a closed-loop interactive
agent framework that integrates planning and recognition to predict what the
user is trying to accomplish and autonomously decide on actions to take in
response to these predictions. Based on a recent demonstration of such an
assistive interactive agent in a turn-based simulated game, we also discuss new
research challenges that are not present in the areas of artificial
intelligence planning or recognition alone.
