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Unbounded-Time Safety Verification of Stochastic Differential Dynamics

In this paper, we propose a method for bounding the probability that a stochastic differential equation (SDE) system violates a safety specification over the infinite time horizon. SDEs are mathematical models of stochastic processes that capture how states evolve continuously in time. They are wide...

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Autores principales: Feng, Shenghua, Chen, Mingshuai, Xue, Bai, Sankaranarayanan, Sriram, Zhan, Naijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7363234/
http://dx.doi.org/10.1007/978-3-030-53291-8_18
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author Feng, Shenghua
Chen, Mingshuai
Xue, Bai
Sankaranarayanan, Sriram
Zhan, Naijun
author_facet Feng, Shenghua
Chen, Mingshuai
Xue, Bai
Sankaranarayanan, Sriram
Zhan, Naijun
author_sort Feng, Shenghua
collection PubMed
description In this paper, we propose a method for bounding the probability that a stochastic differential equation (SDE) system violates a safety specification over the infinite time horizon. SDEs are mathematical models of stochastic processes that capture how states evolve continuously in time. They are widely used in numerous applications such as engineered systems (e.g., modeling how pedestrians move in an intersection), computational finance (e.g., modeling stock option prices), and ecological processes (e.g., population change over time). Previously the safety verification problem has been tackled over finite and infinite time horizons using a diverse set of approaches. The approach in this paper attempts to connect the two views by first identifying a finite time bound, beyond which the probability of a safety violation can be bounded by a negligibly small number. This is achieved by discovering an exponential barrier certificate that proves exponentially converging bounds on the probability of safety violations over time. Once the finite time interval is found, a finite-time verification approach is used to bound the probability of violation over this interval. We demonstrate our approach over a collection of interesting examples from the literature, wherein our approach can be used to find tight bounds on the violation probability of safety properties over the infinite time horizon.
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spelling pubmed-73632342020-07-16 Unbounded-Time Safety Verification of Stochastic Differential Dynamics Feng, Shenghua Chen, Mingshuai Xue, Bai Sankaranarayanan, Sriram Zhan, Naijun Computer Aided Verification Article In this paper, we propose a method for bounding the probability that a stochastic differential equation (SDE) system violates a safety specification over the infinite time horizon. SDEs are mathematical models of stochastic processes that capture how states evolve continuously in time. They are widely used in numerous applications such as engineered systems (e.g., modeling how pedestrians move in an intersection), computational finance (e.g., modeling stock option prices), and ecological processes (e.g., population change over time). Previously the safety verification problem has been tackled over finite and infinite time horizons using a diverse set of approaches. The approach in this paper attempts to connect the two views by first identifying a finite time bound, beyond which the probability of a safety violation can be bounded by a negligibly small number. This is achieved by discovering an exponential barrier certificate that proves exponentially converging bounds on the probability of safety violations over time. Once the finite time interval is found, a finite-time verification approach is used to bound the probability of violation over this interval. We demonstrate our approach over a collection of interesting examples from the literature, wherein our approach can be used to find tight bounds on the violation probability of safety properties over the infinite time horizon. 2020-06-16 /pmc/articles/PMC7363234/ http://dx.doi.org/10.1007/978-3-030-53291-8_18 Text en © The Author(s) 2020 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
spellingShingle Article
Feng, Shenghua
Chen, Mingshuai
Xue, Bai
Sankaranarayanan, Sriram
Zhan, Naijun
Unbounded-Time Safety Verification of Stochastic Differential Dynamics
title Unbounded-Time Safety Verification of Stochastic Differential Dynamics
title_full Unbounded-Time Safety Verification of Stochastic Differential Dynamics
title_fullStr Unbounded-Time Safety Verification of Stochastic Differential Dynamics
title_full_unstemmed Unbounded-Time Safety Verification of Stochastic Differential Dynamics
title_short Unbounded-Time Safety Verification of Stochastic Differential Dynamics
title_sort unbounded-time safety verification of stochastic differential dynamics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7363234/
http://dx.doi.org/10.1007/978-3-030-53291-8_18
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