Design of a Neuronal Training Modeling Language

Exemplified with the AI-Based Dynamic GUI Adaption


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



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


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.


Grum, M., Bender, B., Alfa, A.S., Gronau, N.: A decision maxim for efficient task realization within analytical network infrastructures. Decis. Support Syst. (2018).

Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., Ahmad, I.: Cloud computing pricing models: a survey. Int. J. Grid Distrib. Comput. 6, 93–106 (2013).

Peffers, K., Tuunanen, T., Gengler, C.E., Rossi, M., Hui, W., Virtanen, V., Bragge, J.: The Design Science Research Process: A Model for Producing and Presenting Information Systems Reseach. 1st Int. Conf. Des. Sci. Inf. Syst. Technol. DESRIST. 24, 83–106 (2006).

Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A Design Science Research Methodology for Information Systems Research. Manag. Inf. Syst. 24, 45–78 (2007).

The Theano Development Team: Theano: A Python framework for fast computation of mathematical expressions. ArXiv E-Prints. (2016).

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: Caffe: Convolutional Architecture for Fast Feature Embedding. CoRR. abs/1408.5093, (2014).

Wongsuphasawat, K., Smilkov, D., Wexler, J., Wilson, J., Mané, D., Fritz, D., Krishnan, D., Viégas, F.B., Wattenberg, M.: Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow. IEEE Trans. Vis. Comput. Graph. 24, 1–12 (2018).

Cannon, R.C., Gleeson, P., Crook, S., Ganapathy, G., Marin, B., Piasini, E., Silver, R.A.: LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2. Front. Neuroinformatics. 8, 79 (2014).

Gleeson, P., Crook, S., Cannon, R.C., Hines, M.L., Billings, G.O., Farinella, M., Morse, T.M., Davison, A.P., Ray, S., Bhalla, U.S., Barnes, S.R., Dimitrova, Y.D., Silver, R.A.: 15 NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail. PLOS Comput. Biol. 6, 1–19 (2010).

List, B., Korherr, B.: An Evaluation of Conceptual Business Process Modelling Languages. In: Proceedings of the 2006 ACM Symposium on Applied Computing. pp. 1532–1539. ACM, New York, NY, USA (2006).

Sultanow, E., Zhou, X., Gronau, N., Cox, S.: Modeling of Processes, Systems and Knowledge: a Multi-Dimensional Comparison of 13 Chosen Methods. Int. Rev. Comput. Softw. IRECOS. 3309–3319 (2012).

Grum, M., Gronau, N.: Process Modeling within the Augmented Reality - The Bidirectional Interplay of Two Worlds. In: Proceedings of the Eighth BMSD. pp. 206–214 (2018).

Aalst, W.M.P. van der: Formalization and verification of event-driven process chains. Inf. Softw. Technol. 41, 639–650 (1999).

Rosing, M. von, White, S., Cummins, F., Man, H. de: Business Process Model and Notation (BPMN). In: Rosing, M. von, Scheer, A.-W., and Scheel, H. von (eds.) The Complete Business Process Handbook. pp. 433–457. Morgan Kaufmann, Boston (2015).

Booch, G., Rumbaugh, J., Jacobson, I.: The Unified Modeling Language User Guide. Addison-Wesley (2005).

Karagiannis, D., Telesko, R.: The EU-Project PROMOTE: A Process-oriented Approach for Knowledge Management. In: Proc. of the Third Int. Conf. of Practical Aspects of Knowledge Management. (2000. pp. 9–18 (2000).

Heisig, P.: The GPO-WM® Method for the Integration of Knowledge Management into Business Processes. In: International Conference on Knowledge Management. pp. 331–337, Graz, Austria (2006).

Gronau, N. et al.: Modeling and Analyzing knowledge intensive business processes with KMDL. Comprehensive insights into theory and practice. Gito (2012).

Pogorzelska, B.: Knowledge Modeling Description Language Version 2.2. University of Potsdam, Department of Business Informatics and Electronic Government (2009).

Grum, M., Gronau, N.: Integration of Augmented Reality Technologies in Process Modeling - The Augmentation of Real World Scenarios With the KMDL. In: Proceedings of the Seventh BMSD. pp. 206–214 (2017).

Deru, M., Ndiaye, A.: Deep Learning mit TensorFlow, Keras und TensorFlow.js: Einstieg, Konzepte und KI-Projekte mit Python, JavaScript und HTML5. Rheinwerk Verlag GmbH (2019).

Kruse, R., Borgelt, C., Braune, C., Klawonn, F., Moewes, C., Steinbrecher, M.: Computational Intelligence: Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze. Springer Fachmedien Wiesbaden (2015).



How to Cite

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: