Planning and acting in partially observable stochastic domains

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra
Artificial Intelligence 101(1–2), pages 99-134
May 1998

In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (MDPs) and partially observable MDPs (POMDPs). We then outline a novel algorithm for solving POMDPs off line and show how, in some cases, a finite-memory controller can be extracted from the solution to a POMDP. We conclude with a discussion of how our approach relates to previous work, the complexity of finding exact solutions to POMDPs, and of some possibilities for finding approximate solutions.

keywordsPlanning; Uncertainty; Partially observable Markov decision processes
journal or series
book Artificial Intelligence (AI)