AI Planning, Machine Learning and Process Mining have so far developed into separate research fields. At the same time, many interesting concepts and insights have been gained at the intersection of these areas in recent years. For example, the behavior of future processes is now comprehensively predicted with the aid of Machine Learning. For the practical application of these findings, however, it is also necessary not only to know the expected course, but also to give recommendations and hints for the achievement of goals, i.e. to carry out comprehensive process planning. At the same time, an adequate integration of the aforementioned research fields is still lacking. In this article, we present a research project in which researchers from the AI and BPM field work jointly together. Therefore, we discuss the overall research problem, the relevant fields of research and our overall research framework to automatically derive process models from executional process data, derive subsequent planning problems and conduct automated planning in order to adaptively plan and execute business processes using real-time forecasts.
翻译:AI计划、机器学习和工艺采矿迄今为止已发展成不同的研究领域,同时近年来在这些领域的交叉点上取得了许多令人感兴趣的概念和见解,例如,在机器学习的帮助下,现在全面预测了未来进程的行为,然而,为了实际应用这些结果,不仅有必要了解预期过程,而且有必要提出实现目标的建议和提示,即开展综合过程规划;与此同时,上述研究领域仍然缺乏充分的整合;在本篇文章中,我们介绍了一个研究项目,由AI和BPM实地的研究人员共同开展工作;因此,我们讨论了总体研究问题、相关研究领域和我们的总体研究框架,以便从执行过程数据中自动得出进程模型,提出随后的规划问题,并进行自动化规划,以便利用实时预测适应性地规划和实施业务流程。