Process Pilot: Learning decision policies from event data for process optimization

Abstract

Prescriptive process monitoring (PresPM) studies techniques that leverage event logs to recommend actions at runtime that improve process outcomes. Recent PresPM approaches increasingly rely on reinforcement learning (RL). Because interacting with real-world processes is often infeasible, these approaches typically construct a Markov Decision Process (MDP) from historical event data to simulate the execution environment. However, how to systematically discover such MDPs from event logs, and what characteristics define effective RL-based PresPM pipelines, remain open problems that limit the reliable deployment of these approaches in practice. This paper presents Process Pilot, an RL-based Pre..