  Variable autonomy equips a system, such as a robot, with mixed initiatives
such that it can adjust its independence level based on the task's complexity
and the surrounding environment. Variable autonomy solves two main problems in
robotic planning: the first is the problem of humans being unable to keep focus
in monitoring and intervening during robotic tasks without appropriate human
factor indicators, and the second is achieving mission success in unforeseen
and uncertain environments in the face of static reward structures. An open
problem in variable autonomy is developing robust methods to dynamically
balance autonomy and human intervention in real-time, ensuring optimal
performance and safety in unpredictable and evolving environments. We posit
that addressing unpredictable and evolving environments through an addition of
rule-based symbolic logic has the potential to make autonomy adjustments more
contextually reliable and adding feedback to reinforcement learning through
data from mixed-initiative control further increases efficacy and safety of
autonomous behaviour.
