  Proactively perceiving others' intentions is a crucial skill to effectively
interact in unstructured, dynamic and novel environments. This work proposes a
first step towards embedding this skill in support robots for search and rescue
missions. Predicting the responders' intentions, indeed, will enable
exploration approaches which will identify and prioritise areas that are more
relevant for the responder and, thus, for the task, leading to the development
of safer, more robust and efficient joint exploration strategies. More
specifically, this paper presents an active intention recognition paradigm to
perceive, even under sensory constraints, not only the target's position but
also the first responder's movements, which can provide information on his/her
intentions (e.g. reaching the position where he/she expects the target to be).
This mechanism is implemented by employing an extension of Monte-Carlo-based
planning techniques for partially observable environments, where the reward
function is augmented with an entropy reduction bonus. We test in simulation
several configurations of reward augmentation, both information theoretic and
not, as well as belief state approximations and obtain substantial improvements
over the basic approach.

Robotics;Artificial Intelligence;Human-Computer Interaction;Machine Learning