Abstract
As the system becomes more intelligent and embedded, the operating environment is gradually changed from closed, static, and controllable to more open, dynamic, and difficult to control. As a result, the design of complex software systems is faced with many challenges arising from the uncertainty of the environment (UoE). On the one hand, ignoring the UoE to manually describe requirements is not only a tough job, but it can also hinder the discovery of potential requirements; on the other hand, it is a challenge to integrate the representation of and reasoning of UoE into the process of modeling complex systems. Based on the analysis of the characteristics of complex systems engineering, this paper takes solving the UoE caused by stakeholders preferences and complex environment context as the entry point and designs a fuzzy control decision-making framework. Specifically, the framework contributes to the spiral of complex systems while solving the UoE by constructing a closed-loop intelligent system based on automatic data flow between information space and physical space for environment sensing, uncertainty analysis, requirements mining, fuzzy reasoning, decision execution as well as feedback optimization. Finally, the framework is validated with a concrete example of an adaptive treadmill system based on the support tools developed.
Published Version
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