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

有机计算为复杂的动态系统带来了显着的优势，如减少开发工作，提高适应性和鲁棒性。然而，对于即使在存在故障或失效（失效操作）的情况下也必须保持功能的安全关键系统，需要进一步的属性。这包括即使在非冗余系统资源失效、有机计算运行时环境受损或剩余资源不足以维持所有服务的情况下，也要维护主要核心功能。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，以适当地降级和重新配置系统。拟议的研究项目解决了相应的研究空白及其基于人工DNA的相互作用：（1）基于人工DNA的有机计算系统语义描述方法，（2）使用人工DNA的有机计算系统的高度自动化的诊断技术，以及（3）在高安全性下的此类系统的适配技术-关键应用。人工DNA有机计算系统的语义描述方法是更高语义的基础，基于人工DNA的有机计算系统诊断技术可以利用语义描述自动建立诊断模型。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的故障和语义描述修改人工DNA，以实现重构和降级概念。在项目中，模型和算法将逐步开发，原型实现，并使用示例场景和故障注入实验进行评估。

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.