Incomplete Information in Markovian Decision Models

Abstract

If a set of states is given in a problem of dynamic programming in which each state can be observed only partially, the given model is generally transformed into a new model with completely observed states. In this article a method is introduced with which Markov models of dynamic programming can be transformed and which preserves the Markov property. The method applies to relatively general sets of states.