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Automated robotic process automation: A self-learning approach

: Gao, J.; Zelst, S.J. van; Lu, X.; Aalst, W.M.P. van der


Panetto, H.:
On the Move to Meaningful Internet Systems. OTM 2019 Conferences. Proceedings : Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21–25, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11877)
ISBN: 978-3-030-33245-7 (Print)
ISBN: 978-3-030-33246-4 (Online)
ISBN: 3-030-33245-4
OnTheMove Event (OTM) <2019, Rhodes>
International Conference on Cooperative Information Systems (CoopIS) <27, 2019, Rhodes>
International Conference "Cloud and Trusted Computing" (C&TC) <2019, Rhodes>
International Conference on Ontologies, DataBases, and Applications of Semantics (ODBASE) <2019, Rhodes>
Conference Paper
Fraunhofer FIT ()

Robotic Process Automation (RPA) recently gained a lot of attention, in both industry and academia. RPA embodies a collection of tools and techniques that allow business owners to automate repetitive manual tasks. The intrinsic value of RPA is beyond dispute, e.g., automation reduces errors and costs and thus allows us to increase the overall business process performance. However, adoption of current-generation RPA tools requires a manual effort w.r.t. identification, elicitation and programming of the to-be-automated tasks. At the same time, several techniques exist that allow us to track the exact behavior of users in the front-end, in great detail. Therefore, in this paper, we present a novel end-to-end approach that allows for completely automated, algorithmic RPA-rule deduction, on the basis of captured user behavior. Furthermore, our proposed approach is accompanied by a publicly available proof-of-concept implementation.