Partially Observable Markov Decision Processes for Artificial Intelligence
In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. In many cases, we have developed new ways of viewing the problem that are, perhaps, more consistent with the AI perspective. We begin by introducing the theory of Markov decision processes (MDPs) and partially observable Markov decision processes POMDPs. We then outline a novel algorithm for solving POMDPs off line and show how, in many cases, a finite-memory controller can be extracted from the solution to a POMDP. We conclude with a simple example.