  The Swift Gamma-Ray Burst Explorer is a uniquely capable mission, with three
on-board instruments and rapid slewing capabilities. It serves as a
fast-response satellite observatory for everything from gravitational-wave
counterpart searches to cometary science. Swift averages 125 different
observations per day, and is consistently over-subscribed, responding to about
one-hundred Target of Oportunity (ToO) requests per month from the general
astrophysics community, as well as co-pointing and follow-up agreements with
many other observatories. Since launch in 2004, the demands put on the
spacecraft have grown consistently in terms of number and type of targets as
well as schedule complexity. To facilitate this growth, various scheduling
tools and helper technologies have been built by the Swift team to continue
improving the scientific yield of the Swift mission. However, these tools have
been used only to assist humans in exploring the local pareto surface and for
fixing constraint violations. Because of the computational complexity of the
scheduling task, no automation tool has been able to produce a plan of equal or
higher quality than that produced by a well-trained human, given the necessary
time constraints. In this proceeding we formalize the Swift Scheduling Problem
as a dynamic fuzzy Constraint Satisfaction Problem (DF-CSP) and explore the
global solution space. We detail here several approaches towards achieving the
goal of surpassing human quality schedules using classical optimization and
algorithmic techniques, as well as machine learning and recurrent neural
network (RNN) methods. We then briefly discuss the increased scientific yield
and benefit to the wider astrophysics community that would result from the
further development and adoption of these technologies.
