TY  - CHAP
A1  - Grum, Marcus
A1  - Kotarski, David
A1  - Ambros, Maximilian
A1  - Biru, Tibebu
A1  - Krallmann, Hermann
A1  - Gronau, Norbert
A2  - Shishkov, Boris
T1  - Managing knowledge of intelligent systems
T2  - Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5–7, 2021, Proceedings
N2  - 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.
KW  - domain-specific language
KW  - morphologic box
KW  - explainability
Y1  - 2021
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/60460
SN  - 978-3-030-79975-5
SN  - 978-3-030-79976-2
VL  - 422
SP  - 78
EP  - 96
PB  - Springer International Publishing
CY  - Cham
ER  -