Abstract
The present paper explores the opportunity of applying reinforcement learning to various typical tasks in the field of predictive process monitoring. The tasks considered are the prediction of both next event activity and time completion as well as the prediction of the whole progression of running cases. Experiments have been conducted on the popular benchmark dataset, BPI’ 2012, on which we compare the proposed learning system with state of the art methods adopting LSTM networks trained through supervised learning. Results enlighten promising features of the approach and interesting research issues and challenges, as well as proving the applicability of reinforcement learning to predictive process monitoring.
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Chiorrini, A., Diamantini, C., Mircoli, A., Potena, D. (2021). A Preliminary Study on the Application of Reinforcement Learning for Predictive Process Monitoring. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_10
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