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Asp. Comput."],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Machine learning (ML) is used increasingly in safety-critical\nsystems to provide more complex autonomy to make the system to do\ndecisions by itself in uncertain environments. Using ML to learn\nsystem features is fundamentally different from manually\nimplementing them in conventional components written in source code.\nIn this paper, we make a first step towards exploring\nthe architecture modeling of safety-critical autonomous systems\nwhich are composed of conventional components and ML components,\nbased on natural language requirements. Firstly, augmented\nintelligence for restricted natural language requirement\nmodeling is proposed. In that, several AI technologies such as\nnatural language processing and clustering are used to recommend\ncandidate terms to the glossary, as well as machine learning is used\nto predict the category of requirements. The glossary including data\ndictionary and domain glossary and the category of requirements will\nbe used in the restricted natural language requirement specification\nmethod RNLReq, which is equipped with a set of restriction rules and\ntemplates to structure and restrict the way how users document\nrequirements. Secondly, automatic generation of SysML architecture\nmodels from the RNLReq requirement specifications is presented.\nThirdly, the prototype tool is implemented based on Papyrus.\nFinally,  it presents the evaluation of the proposed\napproach using an industrial autonomous guidance, navigation and\ncontrol case study.<\/jats:p>","DOI":"10.1007\/s00165-021-00543-6","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T09:02:17Z","timestamp":1621242137000},"page":"343-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploiting augmented intelligence in the modeling of safety-critical autonomous systems"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9888-6975","authenticated-orcid":false,"given":"Zhibin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqiang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiu","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Paul","family":"Bodeveix","sequence":"additional","affiliation":[{"name":"IRIT-University of Toulouse, Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mamoun","family":"Filali","sequence":"additional","affiliation":[{"name":"IRIT-University of Toulouse, Toulouse, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zonghua","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Applied Physics and Electronics, Umea University, Ume\u00e5, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","reference":[{"key":"e_1_2_1_2_1_2","doi-asserted-by":"crossref","unstructured":"Aniculaesei A Arnsberger D Howar F Rausch A (2016) Towards the verification of safety-critical autonomous systems in dynamic environments. 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