Design of a Neuronal Training Modeling Language

Exemplified with the AI-Based Dynamic GUI Adaption

Authors

  • Marcus Grum
  • Werner Hiessl
  • Karl Maresch
  • Norbert Gronau

DOI:

https://doi.org/10.30844/aistes.v5i1.20

Keywords:

Artificial Intelligence, Development of AI-based Systems, AI-based Decision Support Systems, Cooperative AI, Human-In-The-Loop, Process-oriented Knowledge Acquisition

Abstract

As the complexity of learning task requirements, computer infrastructures and knowledge acquisition for artificial neuronal networks (ANN) is increasing, the communication about ANN is challenging. 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 management are attractive. With the aim of making the design of learning tasks expressible by models, this paper proposes a graphical modeling language called Neuronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaption exemplifies its use as a first demonstration.

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Published

2021-03-25

How to Cite

[1]
Grum, M., Hiessl, W., Maresch, K. and Gronau, N. 2021. Design of a Neuronal Training Modeling Language: Exemplified with the AI-Based Dynamic GUI Adaption. AIS Transactions on Enterprise Systems. 5, 1 (Mar. 2021). DOI:https://doi.org/10.30844/aistes.v5i1.20.

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