Dynamic process workflow routing using Deep Learning

Kareem Amin; Stelios Kapetanakis; Klaus-Dieter Althoff; Andreas Dengel; Miltos Petridis

In: Artificial Intelligence XXXV. SGAI International Conference on Artificial Intelligence (AI-2018), December 11-13, Cambridge, United Kingdom, Springer, 2018.


Dynamic business processes are challenged by constant changes due to unstable environments, unexpected incidents and difficult to predict behaviours. In industry areas like customer support, complex incidents can be regarded as in-stances of a dynamic process since there can be no static planning against their unique nature. Support engineers will work with any means at their disposal to solve any emerging case and define a custom prioritization strategy, to achieve the best possible result. To assist with this, in this paper we describe a novel workflow application to address the tasks of high solution accuracy and shorter prediction resolution time. We describe how workflows can be generated to assist experts and how our solution can scale over time to produce domain-specific reusable cases for similar problems. Our work is evaluated using data from 5000 workflows from the automotive industry.

Weitere Links

AI_2018_final.pdf (pdf, 315 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence