Learning to act: a Reinforcement Learning approach to recommend the best next activities
This work addresses the need for data-driven recommendations in process management, though it is incremental by applying RL to an existing optimization perspective.
The paper tackles the problem of recommending optimal next activities in business processes by learning an optimal policy using Reinforcement Learning, achieving improved performance on two real-life scenarios.
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.