Smart Sampling for Lightweight Verification of Markov Decision Processes

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.

IEEE conference style, 11 pages, 5 algorithms, 11 figures, 1 table

References in corpus (1)

• An expectation transformer approach to predicate abstraction and data independence for probabilistic programs

Cited by in corpus (2)

• PAC Statistical Model Checking for Markov Decision Processes and Stochastic Games
• 1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization