Planning for Proactive Assistance in Environments with Partial
  Observability
  This paper addresses the problem of synthesizing the behavior of an AI agent
that provides proactive task assistance to a human in settings like factory
floors where they may coexist in a common environment. Unlike in the case of
requested assistance, the human may not be expecting proactive assistance and
hence it is crucial for the agent to ensure that the human is aware of how the
assistance affects her task. This becomes harder when there is a possibility
that the human may neither have full knowledge of the AI agent's capabilities
nor have full observability of its activities. Therefore, our \textit{proactive
assistant} is guided by the following three principles: \textbf{(1)} its
activity decreases the human's cost towards her goal; \textbf{(2)} the human is
able to recognize the potential reduction in her cost; \textbf{(3)} its
activity optimizes the human's overall cost (time/resources) of achieving her
goal. Through empirical evaluation and user studies, we demonstrate the
usefulness of our approach.
