  We present a method for pursuit/evasion that is highly efficient and and
scales to large teams of aircraft. The underlying algorithm is an efficient
algorithm for solving Markov Decision Processes (MDPs) that supports fully
continuous state spaces. We demonstrate the algorithm in a team pursuit/evasion
setting in a 3D environment using a pseudo-6DOF model and study performance by
varying sizes of team members. We show that as the number of aircraft in the
simulation grows, computational performance remains efficient and is suitable
for real-time systems. We also define probability-to-win and survivability
metrics that describe the teams' performance over multiple trials, and show
that the algorithm performs consistently. We provide numerical results showing
control inputs for a typical 1v1 encounter and provide videos for 1v1, 2v2,
3v3, 4v4, and 10v10 contests to demonstrate the ability of the algorithm to
adapt seamlessly to complex environments.
