  The proposed symposium focuses understanding, modeling, and improving the
efficacy of (a) communicating proficiency from human to robot and (b)
communicating intent from a human to a robot. For example, how should a robot
convey predicted ability on a new task? How should it report performance on a
task that was just completed? How should a robot adapt its proficiency criteria
based on human intentions and values?
  Communities in AI, robotics, HRI, and cognitive science have addressed
related questions, but there are no agreed upon standards for evaluating
proficiency and intent-based interactions. This is a pressing challenge for
human-robot interaction for a variety of reasons. Prior work has shown that a
robot that can assess its performance can alter human perception of the robot
and decisions on control allocation. There is also significant evidence in
robotics that accurately setting human expectations is critical, especially
when proficiency is below human expectations. Moreover, proficiency assessment
depends on context and intent, and a human teammate might increase or decrease
performance standards, adapt tolerance for risk and uncertainty, demand
predictive assessments that affect attention allocation, or otherwise reassess
or adapt intent.
