Falsification of cyber-physical systems with reinforcement learning

Koki Kato, Fuyuki Ishikawa, Shinichi Honiden

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

We propose a novel framework for testing configurable cyber-physical systems over a given specification represented as metric temporal logic formula. Given a system model with configurable properties and a specification, our approach first learns to falsify the model by using reinforcement learning technique under a certain variety of configurations. After the training phase, it is expected that the experienced falsification agent can quickly find an input signal such that the output violates the specification, even though the specific configuration is not known to the agent. Thus we can use this agent again and again when different configurations are investigated for a product family or for trials and errors of configuration design. We performed a preliminary experiment to validate our hypothesis that the reinforcement learning technique can be applied for falsification problems.

本文言語English
ホスト出版物のタイトルProceedings - 2018 3rd Workshop on Monitoring and Testing of Cyber-Physical Systems, MT-CPS 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ5-6
ページ数2
ISBN(印刷版)9781538667484
DOI
出版ステータスPublished - 2018 8 7
外部発表はい
イベント3rd Workshop on Monitoring and Testing of Cyber-Physical Systems, MT-CPS 2018 - Porto, Portugal
継続期間: 2018 4 10 → …

出版物シリーズ

名前Proceedings - 2018 3rd Workshop on Monitoring and Testing of Cyber-Physical Systems, MT-CPS 2018

Other

Other3rd Workshop on Monitoring and Testing of Cyber-Physical Systems, MT-CPS 2018
国/地域Portugal
CityPorto
Period18/4/10 → …

ASJC Scopus subject areas

  • 安全性、リスク、信頼性、品質管理
  • 人工知能

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