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2022 | OriginalPaper | Buchkapitel

Learning to Act: A Reinforcement Learning Approach to Recommend the Best Next Activities

verfasst von : Stefano Branchi, Chiara Di Francescomarino, Chiara Ghidini, David Massimo, Francesco Ricci, Massimiliano Ronzani

Erschienen in: Business Process Management Forum

Verlag: Springer International Publishing

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Abstract

The rise of process data availability has recently led to the development of data-driven learning approaches. However, most of these approaches restrict the use of the learned model to predict the future of ongoing process executions. The goal of this paper is moving a step forward and leveraging available data to learning to act, by supporting users with recommendations derived from an optimal strategy (measure of performance). We take the optimization perspective of one process actor and we recommend the best activities to execute next, in response to what happens in a complex external environment, where there is no control on exogenous factors. To this aim, we investigate an approach that learns, by means of Reinforcement Learning, the optimal policy from the observation of past executions and recommends the best activities to carry on for optimizing a Key Performance Indicator of interest. The validity of the approach is demonstrated on two scenarios taken from real-life data.

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Vorheriges Kapitel Alpha Precision: Estimating the Significant System Behavior in a Model

Nächstes Kapitel Change Detection in Dynamic Event Attributes

In this paper we set \(\gamma =1\), hence equally weighting the reward obtained at each action points of the target actor.

The information on the average interest rate is extracted from the BPI2017 [5] dataset which contains data from the same financial institution.

We estimate the average salary of a bank employed in the Netherlands from https://www.salaryexpert.com/salary/job/banking-disbursement-clerk/netherlands.

The complete MDP description is available at tinyurl.com/2p8aytrb.

The MDP actions in this scenario take into account, besides the activity name, also the 2-month interval (since the creation of the fine) in which the activity has been carried out (2months).

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Titel: Learning to Act: A Reinforcement Learning Approach to Recommend the Best Next Activities
verfasst von: Stefano Branchi
Chiara Di Francescomarino
Chiara Ghidini
David Massimo
Francesco Ricci
Massimiliano Ronzani
Verlag: Springer International Publishing
Buch: Business Process Management Forum
Print ISBN: 978-3-031-16170-4

Electronic ISBN: 978-3-031-16171-1

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-16171-1_9

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