This paper discusses on-going work in adaptive architectures concerning automatic adaptation rule derivation. Adaptation is rule-action based but deriving rules that meet the adaptation goals are tedious and error prone. We present an approach that uses model-driven derivation and training for automatically deriving adaptation rules, and exemplify this in an environment for scientific computing.
['Jesper Andersson', 'Morgan Ericsson', 'Welf Löwe']
Automatic Rule Derivation for Adaptive Architectures