  Fully cooperative, partially observable multi-agent problems are ubiquitous
in the real world. In this paper, we focus on a specific subclass of
coordination problems in which humans are able to discover self-explaining
deviations (SEDs). SEDs are actions that deviate from the common understanding
of what reasonable behavior would be in normal circumstances. They are taken
with the intention of causing another agent or other agents to realize, using
theory of mind, that the circumstance must be abnormal. We first motivate SED
with a real world example and formalize its definition. Next, we introduce a
novel algorithm, improvement maximizing self-explaining deviations
(IMPROVISED), to perform SEDs. Lastly, we evaluate IMPROVISED both in an
illustrative toy setting and the popular benchmark setting Hanabi, where it is
the first method to produce so called finesse plays, which are regarded as one
of the more iconic examples of human theory of mind.
