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Exploiting augmented intelligence in the modeling of safety-critical autonomous systems

Abstract : Machine learning (ML) is used increasingly in safety-critical systems to provide more complex autonomy to make the system to do decisions by itself in uncertain environments. Using ML to learn system features is fundamentally different from manually implementing them in conventional components written in source code. In this paper, we make a first step towards exploring the architecture modeling of safety-critical autonomous systems which are composed of conventional components and ML components, based on natural language requirements. Firstly, augmented intelligence for restricted natural language requirement modeling is proposed. In that, several AI technologies such as natural language processing and clustering are used to recommend candidate terms to the glossary, as well as machine learning is used to predict the category of requirements. The glossary including data dictionary and domain glossary and the category of requirements will be used in the restricted natural language requirement specification method RNLReq, which is equipped with a set of restriction rules and templates to structure and restrict the way how users document requirements. Secondly, automatic generation of SysML architecture models from the RNLReq requirement specifications is presented. Thirdly, the prototype tool is implemented based on Papyrus. Finally, it presents the evaluation of the proposed approach using an industrial autonomous guidance, navigation and control case study.
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https://hal.archives-ouvertes.fr/hal-03411215
Contributor : Mamoun Filali-Amine Connect in order to contact the contributor
Submitted on : Friday, November 19, 2021 - 10:43:56 AM
Last modification on : Thursday, November 25, 2021 - 3:03:39 PM

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Zhibin Yang, Yang Bao, Yongqiang Yang, Zhiqiu Huang, Jean-Paul Bodeveix, et al.. Exploiting augmented intelligence in the modeling of safety-critical autonomous systems. Formal Aspects of Computing, Springer Verlag, 2021, 33 (3), pp.343-384. ⟨10.1007/s00165-021-00543-6⟩. ⟨hal-03411215⟩

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