TY  - CONF
A1  - Grum, Marcus
A2  - Shishkov, Boris
T1  - Managing multi-site artificial neural networks’ activation rates and activation cycles
T2  - Business modeling and software design : 14th International Symposium, BMSD 2024, Luxembourg City, Luxembourg, July 1–3, 2024, proceedings
N2  - Traditionally, business models and software designs used to model the usage of artificial intelligence (AI) at a very specific point in the process or rather fix implemented application. Since applications can be based on AI, such as networked artificial neural networks (ANN) on top of which applications are installed, these on-top applications can be instructed directly from their underlying ANN compartments [1]. However, with the integration of several AI-based systems, their coordination is a highly relevant target factor for the operation and improvement of networked processes, such as they can be found in cross-organizational production contexts spanning multiple distributed locations. This work aims to extend prior research on managing artificial knowledge transfers among interlinked AIs as coordination instrument by examining effects of different activation types (respective activation rates and cycles) on by ANN-instructed production machines. In a design-science-oriented way, this paper conceptualizes rhythmic state descriptions for dynamic systems and associated 14 experiment designs. Two experiments have been realized, analyzed and evaluated thereafter in regard with their activities and processes induced. Findings show that the simulator [2] used and experiments designed and realized, here, (I) enable research on ANN activation types, (II) illustrate ANN-based production networks disrupted by activation types and clarify the need for harmonizing them. Further, (III) management interventions are derived for harmonizing interlinked ANNs. This study establishes the importance of site-specific coordination mechanisms and novel forms of management interventions as drivers of efficient artificial knowledge transfer.
Y1  - 2024
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/65336
SN  - 978-3-031-64072-8
SN  - 978-3-031-64073-5
SP  - 258
EP  - 269
PB  - Springer
CY  - Cham
ER  -