Dynamic plan execution strategies allow an autonomous
agent to respond to uncertainties while improving
robustness and reducing the need for an overly
conservative plan. Executives have improved this robustness
by expanding the types of choices made dynamically,
such as selecting alternate methods. However,
in methods to date, these additional choices introduce
substantial run-time latency. This paper presents a
novel system called Drake that makes steps towards executing
an expanded set of choices dynamically without
significant latency.

Drake frames a plan as a Disjunctive Temporal Problem
and executes it with a fast dynamic scheduling algorithm.
Prior work demonstrated an efficient technique
for dynamic execution of one special type of DTPs by
using an off-line compilation step to find the possible
consistent choices and compactly record the differences
between them. Drake extends this work to handle a
more general set of choices by recording the minimal
differences between the solutions which are required at
run-time. On randomly generated structured plans with
choice, we show a reduction in the size of the solution
set of over two orders of magnitude, compared to prior
art.