A Reinforcement Learning Approach to Design Verification Strategies of Engineered Systems

Peng Xu, Alejandro Salado, Guangrui Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

System verification is a critical process in the development of engineered systems. Engineers gain confidence in the correct functionality of the system before it is deployed into operation by executing verification activities. Choosing the right set of verification activities at the right system development stage, that is, designing a verification strategy (VS), is essential to balancing information discovery and verification cost. Only recently, quantitative methods have been proposed to support the design of verification strategies. However, their applicability in real-life scenarios is impractical due to their limited computational efficiency in the high dimensional solution space of the VS selection problem. This paper presents a reinforcement learning (RL) approach to search for a near-optimal VS. Specifically, the VS design problem is formulated as a Markov decision process (MDP) in which a value function is required. Then we combine tree search and a neural network (NN) to design a RL algorithm. In the RL algorithm, the value function is approximated as a NN that is trained in an iterative way. The near-optimal VS can be generated from the trained NN. A case study is presented to show the superiority of the proposed method.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3543-3550
Number of pages8
ISBN (Electronic)9781728185262
DOIs
StatePublished - Oct 11 2020
Externally publishedYes
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: Oct 11 2020Oct 14 2020

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2020-October
ISSN (Print)1062-922X

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Country/TerritoryCanada
CityToronto
Period10/11/2010/14/20

Keywords

  • Markov decision process
  • System verification
  • reinforcement learning
  • verification strategy

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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