Design of a Neuronal Training Modeling Language
Exemplified with the AI-Based Dynamic GUI Adaption
DOI:
https://doi.org/10.30844/aistes.v5i1.20Keywords:
Artificial Intelligence, Development of AI-based Systems, AI-based Decision Support Systems, Cooperative AI, Human-In-The-Loop, Process-oriented Knowledge AcquisitionAbstract
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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