  In many robotic applications, an autonomous agent must act within and explore
a partially observed environment that is unobserved by its human teammate. We
consider such a setting in which the agent can, while acting, transmit
declarative information to the human that helps them understand aspects of this
unseen environment. In this work, we address the algorithmic question of how
the agent should plan out what actions to take and what information to
transmit. Naturally, one would expect the human to have preferences, which we
model information-theoretically by scoring transmitted information based on the
change it induces in weighted entropy of the human's belief state. We formulate
this setting as a belief MDP and give a tractable algorithm for solving it
approximately. Then, we give an algorithm that allows the agent to learn the
human's preferences online, through exploration. We validate our approach
experimentally in simulated discrete and continuous partially observed
search-and-recover domains. Visit http://tinyurl.com/chitnis-corl-18 for a
supplementary video.
