Presentation 2020-03-10
Temporal Logic Falsification for Simulink models based on the hybrid robustness using ChainerRL
Ryota Owaki, Shoji Yuen,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) We present a method of falsification for the hybrid property of Simulink model using deep reinforcement learning. This study realizes falsification based on hybrid robustness reflecting the dependence between continuous variables and discrete variables. We propose hybrid robustness reflecting discrete robustness by updating continuous variables. Reward values derived from such robustness are expected to be effective for stable convergence so thatthe number of simulations is reduced. This paper provides an experimental implementation of falsification using Python’s reinforcement learning library ChainerRL in Simulink.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Simulink / hybrid robustness / Falsification / Reinforcement Learning
Paper # MSS2019-67
Date of Issue 2020-03-02 (MSS)

Conference Information
Committee MSS / NLP
Conference Date 2020/3/9(2days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English) SICE-DES, IEICE-MSS, IEICE-NLP
Chair Shigemasa Takai(Osaka Univ.) / Hiroaki Kurokawa(Tokyo Univ. of Tech.)
Vice Chair Atsuo Ozaki(Osaka Inst. of Tech.) / Kiyohisa Natsume(Kyushu Inst. of Tech.)
Secretary Atsuo Ozaki(Osaka Univ.) / Kiyohisa Natsume(Hokkaido Univ.)
Assistant Naoki Hayashi(Osaka Univ.) / Yutaka Shimada(Saitama Univ.) / Toshikaza Samura(Yamaguchi Univ.)

Paper Information
Registration To Technical Committee on Mathematical Systems Science and its applications / Technical Committee on Nonlinear Problems
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Temporal Logic Falsification for Simulink models based on the hybrid robustness using ChainerRL
Sub Title (in English)
Keyword(1) Simulink
Keyword(2) hybrid robustness
Keyword(3) Falsification
Keyword(4) Reinforcement Learning
1st Author's Name Ryota Owaki
1st Author's Affiliation Nagoya University(NU)
2nd Author's Name Shoji Yuen
2nd Author's Affiliation Nagoya University(NU)
Date 2020-03-10
Paper # MSS2019-67
Volume (vol) vol.119
Number (no) MSS-470
Page pp.pp.53-58(MSS),
#Pages 6
Date of Issue 2020-03-02 (MSS)