@article{GrumHiesslMareschetal.2021,
  author    = {Grum, Marcus and Hiessl, Werner and Maresch, Karl and Gronau, Norbert},
  title     = {Design of a neuronal training modeling language},
  series = {AIS-Transactions on enterprise systems},
  volume    = {5},
  journal   = {AIS-Transactions on enterprise systems},
  number    = {1},
  publisher = {GITO-Publ., Verl. f{\"u}r Industrielle Informationstechnik und Organisation},
  address   = {Berlin},
  issn      = {1867-7134},
  doi       = {10.30844/aistes.v5i1.20},
  pages     = {16},
  year      = {2021},
  abstract  = {As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration.},
  language  = {en}
}
@article{VogelGiese2014,
  author    = {Vogel, Thomas and Giese, Holger},
  title     = {Model-Driven engineering of self-adaptive software with EUREMA},
  series = {ACM transactions on autonomous and adaptive systems},
  volume    = {8},
  journal   = {ACM transactions on autonomous and adaptive systems},
  number    = {4},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  issn      = {1556-4665},
  doi       = {10.1145/2555612},
  pages     = {33},
  year      = {2014},
  abstract  = {The development of self-adaptive software requires the engineering of an adaptation engine that controls the underlying adaptable software by feedback loops. The engine often describes the adaptation by runtime models representing the adaptable software and by activities such as analysis and planning that use these models. To systematically address the interplay between runtime models and adaptation activities, runtime megamodels have been proposed. A runtime megamodel is a specific model capturing runtime models and adaptation activities. In this article, we go one step further and present an executable modeling language for ExecUtable RuntimE MegAmodels (EUREMA) that eases the development of adaptation engines by following a model-driven engineering approach. We provide a domain-specific modeling language and a runtime interpreter for adaptation engines, in particular feedback loops. Megamodels are kept alive at runtime and by interpreting them, they are directly executed to run feedback loops. Additionally, they can be dynamically adjusted to adapt feedback loops. Thus, EUREMA supports development by making feedback loops explicit at a higher level of abstraction and it enables solutions where multiple feedback loops interact or operate on top of each other and self-adaptation co-exists with offline adaptation for evolution.},
  language  = {en}
}