Comprehensible and dependable self-learning self-adaptive systems

Self-adaptivity enables flexible solutions in dynamically changing environments. However, due to the increasing complexity, uncertainty, and topology changes in cyber-physical systems (CPS), static adaptation mechanisms are insufficient as they do not always achieve appropriate effects. Furthermore, CPS are used in safety-critical domains, which requires them and their autonomous adaptations to be dependable. To overcome these problems, we extend the MAPE-K feedback loop architecture by imposing a structure and requirements on the knowledge base and by introducing a meta-adaptation layer. This enables us to continuously evaluate the accuracy of previous adaptations, learn new adaptation rules based on executable run-time models, and verify the correctness of the adaptation logic in the current system context. We demonstrate the effectiveness of our approach using a temperature control system. With our framework, we enable the design of comprehensible and dependable dynamically evolving adaptation logics.