  Often adaptive, distributed control can be viewed as an iterated game between
independent players. The coupling between the players' mixed strategies,
arising as the system evolves from one instant to the next, is determined by
the system designer. Information theory tells us that the most likely joint
strategy of the players, given a value of the expectation of the overall
control objective function, is the minimizer of a Lagrangian function of the
joint strategy. So the goal of the system designer is to speed evolution of the
joint strategy to that Lagrangian minimizing point, lower the expectated value
of the control objective function, and repeat. Here we elaborate the theory of
algorithms that do this using local descent procedures, and that thereby
achieve efficient, adaptive, distributed control.
