A methodology for the adaptive control of Markov chains under partial state information

A stochastic adaptive control problem where complete state information is not available to the controller is considered. The system is modeled as a finite stochastic automaton. These models are a slight generalization of the more common partially observable controlled Markov chain models.<<ETX>>