# Permissive Finite-State Controllers of POMDPs using Parameter Synthesis

arXiv: https://arxiv.org/abs/1710.10294 · HTML: https://mlsanity.com/paper/1710.10294/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Learning in Observable POMDPs, without Computationally Intractable Oracles](https://mlsanity.com/paper/2206.03446/) (2022)
- [Convex Optimization for Parameter Synthesis in MDPs](https://mlsanity.com/paper/2107.00108/) (2021)
- [Task-Guided IRL in POMDPs that Scales](https://mlsanity.com/paper/2301.01219/) (2022)
- [Scaling Observation-aware Planning in Uncertain Domains](https://mlsanity.com/paper/2605.22364/) (2026)

## Abstract (arXiv)

We study finite-state controllers (FSCs) for partially observable Markov decision processes (POMDPs) that are provably correct with respect to given specifications. The key insight is that computing (randomised) FSCs on POMDPs is equivalent to - and computationally as hard as - synthesis for parametric Markov chains (pMCs). This correspondence allows to use tools for parameter synthesis in pMCs to compute correct-by-construction FSCs on POMDPs for a variety of specifications. Our experimental evaluation shows comparable performance to well-known POMDP solvers.