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.
Information Sciences 606, 250–271
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.