# Planning with Partially Observable Markov Decision Processes: Advances in Exact Solution Method

arXiv: https://arxiv.org/abs/1301.7417 · HTML: https://mlsanity.com/paper/1301.7417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2013.

## Built on by (most cited first, 1 of 1)

- [Under-Approximating Expected Total Rewards in POMDPs](https://mlsanity.com/paper/2201.08772/) (2022)

## Abstract (arXiv)

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.