  We focus on the problem of designing an artificial agent (AI), capable of
assisting a human user to complete a task. Our goal is to guide human users
towards optimal task performance while keeping their cognitive load as low as
possible. Our insight is that doing so requires an understanding of human
decision making for the task domain at hand. In this work, we consider the
domain of collaborative packing, in which an AI agent provides placement
recommendations to a human user. As a first step, we explore the mechanisms
underlying human packing strategies. We conducted a user study in which 100
human participants completed a series of packing tasks in a virtual
environment. We analyzed their packing strategies and discovered spatial and
temporal patterns, such as that humans tend to place larger items at corners
first. We expect that imbuing an artificial agent with an understanding of this
spatiotemporal structure will enable improved assistance, which will be
reflected in the task performance and the human perception of the AI. Ongoing
work involves the development of a framework that incorporates the extracted
insights to predict and manipulate human decision making towards an efficient
trajectory of low cognitive load and high efficiency. A follow-up study will
evaluate our framework against a set of baselines featuring alternative
strategies of assistance. Our eventual goal is the deployment and evaluation of
our framework on an autonomous robotic manipulator, actively assisting users on
a packing task.
