  There is a growing interest in developing automated agents that can work
alongside humans. In addition to completing the assigned task, such an agent
will undoubtedly be expected to behave in a manner that is preferred by the
human. This requires the human to communicate their preferences to the agent.
To achieve this, the current approaches either require the users to specify the
reward function or the preference is interactively learned from queries that
ask the user to compare behavior. The former approach can be challenging if the
internal representation used by the agent is inscrutable to the human while the
latter is unnecessarily cumbersome for the user if their preference can be
specified more easily in symbolic terms. In this work, we propose PRESCA
(PREference Specification through Concept Acquisition), a system that allows
users to specify their preferences in terms of concepts that they understand.
PRESCA maintains a set of such concepts in a shared vocabulary. If the relevant
concept is not in the shared vocabulary, then it is learned. To make learning a
new concept more feedback efficient, PRESCA leverages causal associations
between the target concept and concepts that are already known. In addition, we
use a novel data augmentation approach to further reduce required feedback. We
evaluate PRESCA by using it on a Minecraft environment and show that it can
effectively align the agent with the user's preference.
