  The integration of manned and unmanned aircraft can help improve wildfire
response. Wildfire containment failures occur when resources available to first
responders, who execute the initial stages of wildfire management referred to
as the initial attack, are ineffective or insufficient. Initial attack
surveillance and suppression models have linked action spaces and objectives,
making their optimization computationally challenging. The initial attack may
be formulated as a multi-agent partially observable Markov decision process
(MPOMDP). We divide the initial attack MPOMDP into surveillance and suppression
processes with their respective planners operating on different, but constant,
time scales. A hierarchical framework iterates between surveillance and
suppression planners while also providing collision avoidance. This framework
is exemplified by a set of multi-rotor unmanned aircraft surveying an initial
attack fire while a manned helicopter suppresses the fire with a water bucket.
Wildfire-specific solver extensions are formulated to reduce the otherwise vast
action spaces. The hierarchical framework outperforms firefighting techniques
and a myopic baseline by up to 242% for moderate wildfires and 60% for rapid
wildfires when simulated in abstracted and actual case studies. We also
validate the early dispatching of additional suppression assets using
regression models to ensure wildfire containment to thresholds established by
wildfire agencies.
