Permissive Finite-State Controllers of POMDPs using Parameter Synthesis

TL;DR

This paper introduces a method for designing finite-state controllers for POMDPs using parameter synthesis techniques from parametric Markov chains, enabling correct-by-construction solutions with competitive performance.

Contribution

It establishes a novel equivalence between FSC synthesis for POMDPs and parameter synthesis for pMCs, allowing the use of existing tools for controller design.

Findings

Comparable performance to established POMDP solvers

Provides provably correct FSCs for various specifications

Leverages parameter synthesis tools for controller construction

Abstract

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.