Probabilistic model checking and autonomy

Kwiatkowska, M., Norman, G. and Parker, D. (2022) Probabilistic model checking and autonomy. Annual Review of Control, Robotics, and Autonomous Systems, 5, (doi: 10.1146/annurev-control-042820-010947)

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The design and control of autonomous systems that operate in uncertain or adversarial environments can be facilitated by formal modeling 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 synthesize an optimal strategy for its control. This method has recently been extended to multiagent systems that exhibit competitive or cooperative behavior modeled via stochastic games and synthesis of equilibria strategies. In this article, we provide an overview of probabilistic model checking, focusing on models supported by the PRISM and PRISM-games model checkers. This overview 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. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see for revised estimates.

Item Type:Articles
Additional Information:This project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement 834115) and the Engineering and Physical Sciences Research Council Programme Grant on Mobile Autonomy (EP/M019918/1).
Glasgow Author(s) Enlighten ID:Norman, Dr Gethin
Authors: Kwiatkowska, M., Norman, G., and Parker, D.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Annual Review of Control, Robotics, and Autonomous Systems
Publisher:Annual Reviews
ISSN (Online):2573-5144
Published Online:06 December 2021
Copyright Holders:Copyright © 2021 Annual Reviews
First Published:First published in Annual Review of Control, Robotics, and Autonomous Systems 5:
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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