Probabilistic Model Checking of Robots Deployed in Extreme Environments

Xingyu Zhao, Valentin Robu, David Flynn, Fateme Dinmohammadi, Michael Fisher, Matt Webster

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Abstract

Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans. This requires a high degree of operational autonomy under uncertain conditions, and poses new challenges for assuring the robot’s safety and reliability. In this paper, we develop a framework for probabilistic model checking on a layered Markov model to verify the safety and reliability requirements of such robots, both at pre-mission stage and during runtime. Two novel estimators based on conservative Bayesian inference and imprecise probability model with sets of priors are introduced to learn the unknown transition parameters from operational data. We demonstrate our approach using data from a real-world deployment of unmanned underwater vehicles in extreme environments.
Original languageEnglish
Title of host publicationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Pages8066-8074
Number of pages9
ISBN (Print)9781577358091
DOIs
Publication statusPublished - 17 Jul 2019
Event33rd AAAI Conference on Artificial Intelligence 2019 - Honolulu, United States
Duration: 27 Jan 20191 Feb 2019

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
Number1
Volume33
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference33rd AAAI Conference on Artificial Intelligence 2019
Abbreviated titleAAAI-19
CountryUnited States
CityHonolulu
Period27/01/191/02/19

Keywords

  • Probabilistic model checking
  • Conservative Bayesian Inference
  • Imprecise Probability
  • Extreme Environments
  • robust estimation
  • runtime verification
  • safety-critical systems
  • safe AI
  • Assurance

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  • Cite this

    Zhao, X., Robu, V., Flynn, D., Dinmohammadi, F., Fisher, M., & Webster, M. (2019). Probabilistic Model Checking of Robots Deployed in Extreme Environments. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (pp. 8066-8074). (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 33, No. 1). AAAI Press. https://doi.org/10.1609/aaai.v33i01.33018066