Exploiting augmented intelligence in the modeling of safety-critical autonomous systems
2021
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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