Strategy synthesis for partially-known switched stochastic systems

John Jackson, Luca Laurenti, Eric Frew, Morteza Lahijanian

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
15 Downloads (Pure)

Abstract

We present a data-driven framework for strategy synthesis for partially-known switched stochastic systems. The properties of the system are specified using linear temporal logic (LTL) over finite traces (LTLf), which is as expressive as LTL and enables interpretations over finite behaviors. The framework first learns the unknown dynamics via Gaussian process regression. Then, it builds a formal abstraction of the switched system in terms of an uncertain Markov model, namely an Interval Markov Decision Process (IMDP), by accounting for both the stochastic behavior of the system and the uncertainty in the learning step. Then, we synthesize a strategy on the resulting IMDP that maximizes the satisfaction probability of the LTLf specification and is robust against all the uncertainties in the abstraction. This strategy is then refined into a switching strategy for the original stochastic system. We show that this strategy is near-optimal and provide a bound on its distance (error) to the optimal strategy. We experimentally validate our framework on various case studies, including both linear and non-linear switched stochastic systems.

Original languageEnglish
Title of host publicationProceedings of the 24th International Conference on Hybrid Systems (HSCC 2021)
Subtitle of host publicationComputation and Control (part of CPS-IoT Week)
PublisherAssociation for Computing Machinery (ACM)
Number of pages11
ISBN (Electronic)978-1-4503-8339-4
DOIs
Publication statusPublished - 2021
Event24th ACM International Conference on Hybrid Systems Computation and Control, HSCC 2021, held as part of the 14th Cyber Physical Systems and Internet-of-Things Week, CPS-IoT Week 2021 - Virtual, Online, United States
Duration: 19 May 202121 May 2021

Conference

Conference24th ACM International Conference on Hybrid Systems Computation and Control, HSCC 2021, held as part of the 14th Cyber Physical Systems and Internet-of-Things Week, CPS-IoT Week 2021
Country/TerritoryUnited States
CityVirtual, Online
Period19/05/2121/05/21

Keywords

  • formal synthesis
  • gaussian process regression
  • safe autonomy
  • switched stochastic systems
  • uncertain markov decision processes

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