Comprehensible and dependable self-learning self-adaptive systems
| Autor: | Verena Klös, Thomas Göthel, Sabine Glesner |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Structure (mathematical logic)
Correctness Computer science business.industry Distributed computing 020207 software engineering Context (language use) 02 engineering and technology computer.file_format Feedback loop Knowledge base Hardware and Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Executable Layer (object-oriented design) Adaptation (computer science) business computer Software |
| Zdroj: | Journal of Systems Architecture. :28-42 |
| ISSN: | 1383-7621 |
| DOI: | 10.1016/j.sysarc.2018.03.004 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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