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Related Experiment Video

Updated: Jul 19, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning.

Alexandros Bousdekis1, Athanasios Kerasiotis1, Silvester Kotsias1

  • 1Department of Informatics and Computer Engineering, University of West Attica, 12242 Egaleo, Greece.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces reinforcement learning (RL) for predictive business process monitoring, addressing the complexity and deterministic limitations of traditional process mining. The novel approach enhances process analysis and prediction capabilities.

Keywords:
business process managementdata analyticsmachine learningpredictive business process monitoringprocess mining

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Business Process Management

Background:

  • Process mining analyzes business processes from event logs to discover, monitor, and improve them.
  • Traditional process discovery methods yield complex, hard-to-understand models and lack predictive capabilities due to their deterministic nature.
  • Existing methods struggle with uncertainty and cannot predict future process behavior.

Purpose of the Study:

  • To develop a novel predictive business process monitoring approach.
  • To leverage reinforcement learning (RL) for enhanced process analysis and prediction.
  • To address the limitations of traditional process mining in handling complexity and uncertainty.

Main Methods:

  • Utilized reinforcement learning (RL) techniques for predictive process monitoring.
  • Developed a new approach to model and predict business process behavior.
  • Evaluated the proposed method within the banking sector using a practical use case.

Main Results:

  • Demonstrated the feasibility of using reinforcement learning for predictive business process monitoring.
  • The proposed RL-based approach offers a potential solution to the complexity and deterministic limitations of conventional methods.
  • The use case in the banking sector provided insights into the practical application and effectiveness of the approach.

Conclusions:

  • Reinforcement learning presents a promising avenue for advancing predictive business process monitoring.
  • The developed approach can potentially lead to more understandable and predictive process models.
  • Further research in this area could significantly improve business process management and operational efficiency.