  We consider a moving target that we seek to learn from samples. Our results
extend randomized techniques developed in control and optimization for a
constant target to the case where the target is changing. We derive a novel
bound on the number of samples that are required to construct a probably
approximately correct (PAC) estimate of the target. Furthermore, when the
moving target is a convex polytope, we provide a constructive method of
generating the PAC estimate using a mixed integer linear program (MILP). The
proposed method is demonstrated on an application to autonomous emergency
braking.
