@incollection{GrumKotarskiAmbrosetal.2021,
  author    = {Grum, Marcus and Kotarski, David and Ambros, Maximilian and Biru, Tibebu and Krallmann, Hermann and Gronau, Norbert},
  title     = {Managing knowledge of intelligent systems},
  series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings},
  volume    = {422},
  booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings},
  editor    = {Shishkov, Boris},
  publisher = {Springer International Publishing},
  address   = {Cham},
  isbn      = {978-3-030-79975-5},
  doi       = {10.1007/978-3-030-79976-2_5},
  pages     = {78 -- 96},
  year      = {2021},
  abstract  = {Since more and more business tasks are enabled by Artificial Intelligence (AI)-based techniques, the number of knowledge-intensive tasks increase as trivial tasks can be automated and non-trivial tasks demand human-machine interactions. With this, challenges regarding the management of knowledge workers and machines rise [9]. Furthermore, knowledge workers experience time pressure, which can lead to a decrease in output quality. Artificial Intelligence-based systems (AIS) have the potential to assist human workers in knowledge-intensive work. By providing a domain-specific language, contextual and situational awareness as well as their process embedding can be specified, which enables the management of human and AIS to ease knowledge transfer in a way that process time, cost and quality are improved significantly. This contribution outlines a framework to designing these systems and accounts for their implementation.},
  language  = {en}
}