在不确定性下的业务流程的建模和预测监测与强化学习
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
概括
本研究介绍了用于预测业务流程监控的强化学习 (RL),解决了传统过程采矿的复杂性和决定性限制. 这种新的方法增强了过程分析和预测能力.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 业务流程管理 业务流程管理
背景情况:
- 过程挖掘从事件日志分析业务流程,以发现,监控和改进它们.
- 传统的过程发现方法产生复杂的,难以理解的模型,由于其决定性性质,缺乏预测能力.
- 现有的方法与不确定性作斗争,无法预测未来的过程行为.
研究的目的:
- 开发一种新的预测性业务流程监控方法.
- 利用强化学习 (RL) 进行增强的过程分析和预测.
- 解决传统过程采矿在处理复杂性和不确定性方面的局限性.
主要方法:
- 利用强化学习 (RL) 技术进行预测过程监控.
- 开发了一种新的方法来建模和预测业务流程行为.
- 在银行部门使用实际用例评估拟议的方法.
主要成果:
- 证明了用于预测业务流程监控的强化学习的可行性.
- 提出的基于RL的方法为传统方法的复杂性和决定性限制提供了潜在的解决方案.
- 银行业的用例提供了对该方法的实际应用和有效性的见解.
结论:
- 强化学习为推进预测业务流程监控提供了一个有希望的途径.
- 开发的方法可能会导致更易于理解和预测的过程模型.
- 在这个领域进行进一步的研究可以显著提高业务流程管理和运营效率.
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