  Physical agents that can autonomously generate engaging, life-like behaviour
will lead to more responsive and interesting robots and other autonomous
systems. Although many advances have been made for one-to-one interactions in
well controlled settings, future physical agents should be capable of
interacting with humans in natural settings, including group interaction. In
order to generate engaging behaviours, the autonomous system must first be able
to estimate its human partners' engagement level. In this paper, we propose an
approach for estimating engagement during group interaction by simultaneously
taking into account active and passive interaction, i.e. occupancy, and use the
measure as the reward signal within a reinforcement learning framework to learn
engaging interactive behaviours. The proposed approach is implemented in an
interactive sculptural system in a museum setting. We compare the learning
system to a baseline using pre-scripted interactive behaviours. Analysis based
on sensory data and survey data shows that adaptable behaviours within an
expert-designed action space can achieve higher engagement and likeability.
