Smart Sampling for Lightweight Verification of Markov Decision Processes
Data Structures and Algorithms
2016-11-15 v2
Abstract
Markov decision processes (MDP) are useful to model optimisation problems in concurrent systems. To verify MDPs with efficient Monte Carlo techniques requires that their nondeterminism be resolved by a scheduler. Recent work has introduced the elements of lightweight techniques to sample directly from scheduler space, but finding optimal schedulers by simple sampling may be inefficient. Here we describe "smart" sampling algorithms that can make substantial improvements in performance.
Cite
@article{arxiv.1409.2116,
title = {Smart Sampling for Lightweight Verification of Markov Decision Processes},
author = {Pedro D'Argenio and Axel Legay and Sean Sedwards and Louis-Marie Traonouez},
journal= {arXiv preprint arXiv:1409.2116},
year = {2016}
}
Comments
IEEE conference style, 11 pages, 5 algorithms, 11 figures, 1 table