  Synchronizing expectations and knowledge about the state of the world is an
essential capability for effective collaboration. For robots to effectively
collaborate with humans and other autonomous agents, it is critical that they
be able to generate intelligible explanations to reconcile differences between
their understanding of the world and that of their collaborators. In this work
we present Single-shot Policy Explanation for Augmenting Rewards (SPEAR), a
novel sequential optimization algorithm that uses semantic explanations derived
from combinations of planning predicates to augment agents' reward functions,
driving their policies to exhibit more optimal behavior. We provide an
experimental validation of our algorithm's policy manipulation capabilities in
two practically grounded applications and conclude with a performance analysis
of SPEAR on domains of increasingly complex state space and predicate counts.
We demonstrate that our method makes substantial improvements over the
state-of-the-art in terms of runtime and addressable problem size, enabling an
agent to leverage its own expertise to communicate actionable information to
improve another's performance.
