  We describe a method for time-critical decision making involving sequential
tasks and stochastic processes. The method employs several iterative refinement
routines for solving different aspects of the decision making problem. This
paper concentrates on the meta-level control problem of deliberation
scheduling, allocating computational resources to these routines. We provide
different models corresponding to optimization problems that capture the
different circumstances and computational strategies for decision making under
time constraints. We consider precursor models in which all decision making is
performed prior to execution and recurrent models in which decision making is
performed in parallel with execution, accounting for the states observed during
execution and anticipating future states. We describe algorithms for precursor
and recurrent models and provide the results of our empirical investigations to
date.
