FORLAPS combines offline Q-learning with process-aware augmentation and fine-tuning, claiming 31% resource time savings and 23% process time reduction in prescriptive process monitoring.
(Eds.), Business Process Management Forum, Springer International Publishing
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An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring
FORLAPS combines offline Q-learning with process-aware augmentation and fine-tuning, claiming 31% resource time savings and 23% process time reduction in prescriptive process monitoring.