Planning for Proactive Assistance in Environments with Partial Observability
This addresses the challenge of human-AI collaboration in settings where humans may not fully observe or understand the agent's actions, though it appears incremental in applying existing principles to proactive assistance.
The paper tackles the problem of designing an AI agent that provides proactive assistance to humans in partially observable environments, such as factory floors, by ensuring the assistance reduces the human's task cost and is recognizable to them. It demonstrates the approach's usefulness through empirical evaluation and user studies.
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.