Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method
Artificial Intelligence
2013-02-01 v1
Abstract
There is much interest in using partially observable Markov decision processes (POMDPs) as a formal model for planning in stochastic domains. This paper is concerned with finding optimal policies for POMDPs. We propose several improvements to incremental pruning, presently the most efficient exact algorithm for solving POMDPs.
Cite
@article{arxiv.1301.7417,
title = {Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method},
author = {Nevin Lianwen Zhang and Stephen S. Lee},
journal= {arXiv preprint arXiv:1301.7417},
year = {2013}
}
Comments
Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)