Collaborative embedded systems (CES) typically operate in highly dynamic contexts that cannot be completely predicted during design time. These systems are subject to a wide range of uncertainties occurring at runtime, which can be distinguished in aleatory or epistemic. While aleatory uncertainty refers to stochasticity that is present in natural or physical processes and systems, epistemic uncertainty refers to the knowledge that is available to the system, for example, in the form of an ontology, being insufficient for the functionalities that require certain knowledge. Even though both of these two kinds of uncertainties are relevant for CES, epistemic uncertainties are especially important, since forming collaborative system groups requires a structured exchange of information. In the autonomous driving domain for instance, the information exchange between different CES of different vehicles may be related to own or environmental behavior, goals or functionalities. By today, the systematic identification of epistemic uncertainties sourced in the information exchange is insufficiently explored, as only some specialized classifications for uncertainties in the area of self-adaptive systems exist. This paper contributes an epistemic uncertainty classification scheme for runtime information exchange (EURECA) in collaborative system groups. By using this classification scheme, it is possible to identify the relevant epistemic sources of uncertainties for a CES during requirements engineering.

中文翻译：

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EURECA：用于协作系统组中运行时信息交换的认知不确定性分类方案

协作嵌入式系统（CES）通常在无法在设计时完全预测的高度动态的环境中运行。这些系统在运行时会受到各种不确定性的影响，这些不确定性可以通过偶然的或认知的方式加以区分。偶然不确定性是指自然或物理过程和系统中存在的随机性，而认知不确定性是指系统可用的知识，例如以本体形式存在的知识，不足以需要某些知识的功能。即使这两种不确定性都与CES有关，但认知不确定性尤为重要，因为形成协作系统组需要结构化的信息交换。例如在自动驾驶领域，不同车辆的不同CES之间的信息交换可能与自己或环境的行为，目标或功能有关。到今天，由于在自适应系统领域中仅存在一些不确定性的专门分类，因此对信息交换中所产生的认知不确定性的系统识别还没有得到足够的研究。本文为È pistemic ü ncertainty分级方案- [R不定时信息Ë X Ç ħ一个NGE（EURECA）协作系统的基团。通过使用这种分类方案，可以在需求工程过程中为CES确定不确定性的相关认知来源。