Probabilistic Model Checking and Autonomy

TL;DR

This paper reviews probabilistic model checking techniques for autonomous systems, emphasizing models like MDPs and stochastic games, and discusses their application, challenges, and future research directions.

Contribution

It provides an overview of probabilistic model checking tools and models for autonomous systems, including recent extensions to multi-agent stochastic games.

Findings

Applicability demonstrated through autonomous system examples

Coverage of models supported by PRISM and PRISM-games

Identification of key research challenges and future directions

Abstract

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.