Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches
arXiv:2303.09209v2 Announce Type: replace Abstract: Prescriptive Process Monitoring is an emerging area within Process Mining that focuses on recommending actions to optimize business outcomes. Most existing works prescribe pre-defined interventions, i.e., sets of actions applied to ongoing process executions to achieve a specific objective or Key Performance Indicator (KPI). In contrast, only a few approaches have explored learning and evaluating optimal behavioral policies, i.e., general strategies that determine the best sequence of actions to maximize a desired KPI. In this paper, we addre
The proliferation of digital event logs in business processes, combined with advancements in reinforcement learning, enables more sophisticated approaches to prescriptive process monitoring.
Learning optimal behavioral policies from event logs can lead to automation of complex decision-making in business processes, directly impacting efficiency and Key Performance Indicator (KPI) optimization.
This research suggests a move from pre-defined interventions in process monitoring to dynamic, adaptive strategies determined by AI, potentially enabling more intelligent and autonomous systems.
- · AI software providers
- · Process mining companies
- · Organizations with complex operational workflows
- · Traditional business process consultants lacking AI expertise
- · Static rule-based automation platforms
Companies will begin to integrate sophisticated AI models to dynamically optimize their operational processes based on real-time event data.
This integration could lead to significant reductions in manual oversight and human intervention in routine process management, freeing up resources.
The ability of AI to learn optimal policies might reshape organizational structures, decentralizing decision-making to autonomous agentic systems.
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Read at arXiv cs.AI