Organic Computing with Artificial DNA for Reliable Dynamic Systems based on Semantic Models and Evolutionary Algorithms for Fault Diagnosis and Adaptation

有机计算为复杂动态系统带来了显著的优势，如减少开发工作量、提高适应性和健壮性。然而，对于即使在出现故障或故障(故障-操作)时也必须保持功能的安全关键系统，需要更多的属性。这包括维护主要核心功能，即使非冗余系统资源出现故障、有机计算运行时环境受到损害或剩余资源不足以维护所有服务。这些故障场景需要系统的语义知识与故障诊断和自适应技术相结合，以适当地降低和重新配置系统。提出的研究项目基于人工DNA解决了相应的研究空白及其相互作用：(1)基于人工DNA的有机计算系统的语义描述方法，(2)基于人工DNA的有机计算系统的高度自动化的诊断技术，基于人工DNA的有机计算系统的语义描述方法是基于语义的故障检测和自适应技术的基础，基于人工DNA的有机计算系统诊断技术可以利用人工DNA的语义描述自动构建诊断模型。此外，这些模型可以通过进化算法进行优化，以提高其故障检测率；自适应技术根据识别出的故障和语义描述修改人工DNA，实现重构和退化概念；在项目中，将通过样本场景和故障注入实验对模型和算法进行增量开发、原型实现和评估。

Organic Computing leads to significant advantages for complex dynamic systems like reduced development efforts, increased adaptability and robustness. However, for safety-critical systems which have to maintain functionality even in the presence of faults or failures (fail-operational) further properties are necessary. This includes the maintenance of the major core functionality even if non redundant system resources fail, the organic computing run-time environment is harmed or the remaining resources are insufficient to maintain all services. These failure scenarios require semantic knowledge of the system combined with fault-diagnosis and adaption techniques to properly degrade and reconfigure the system.The proposed research project addresses the corresponding research gaps and their interactions based on artificial DNA: (1) semantic description methods for organic computing systems based on artificial DNA, (2) diagnosis techniques with a high level of automation for organic computing systems using artificial DNA, and (3) adaptation techniques for such systems in highly safety-critical applications.Semantic description methods for organic computing systems with artificial DNA are the foundation for higher semantic-based failure detection and adaptation techniques.Diagnosis techniques for organic computing systems with artificial DNA can exploit the semantic descriptions to automatically build diagnosis models. Furthermore, these models can be optimized by evolutionary algorithms to improve their failure detection rates.Adaptation techniques modify the artificial DNA based on the recognized failures and the semantic description to realize the reconfiguration and degradation concepts.Within the project, the models and algorithms will be incrementally developed, prototypically implemented and evaluated using sample scenarios and failure injection experiments.