  Design and control of autonomous systems that operate in uncertain or
adversarial environments can be facilitated by formal modelling and analysis.
Probabilistic model checking is a technique to automatically verify, for a
given temporal logic specification, that a system model satisfies the
specification, as well as to synthesise an optimal strategy for its control.
This method has recently been extended to multi-agent systems that exhibit
competitive or cooperative behaviour modelled via stochastic games and
synthesis of equilibria strategies. In this paper, we provide an overview of
probabilistic model checking, focusing on models supported by the PRISM and
PRISM-games model checkers. This includes fully observable and partially
observable Markov decision processes, as well as turn-based and concurrent
stochastic games, together with associated probabilistic temporal logics. We
demonstrate the applicability of the framework through illustrative examples
from autonomous systems. Finally, we highlight research challenges and suggest
directions for future work in this area.
