论文标题
学习行动：一种加强学习方法，推荐最佳下一个活动
Learning to act: a Reinforcement Learning approach to recommend the best next activities
论文作者
论文摘要
流程数据可用性的兴起最近导致了数据驱动的学习方法的发展。但是，这些方法中的大多数限制了学习模型的使用来预测正在进行的过程执行的未来。本文的目的是向前迈出一步，并利用可用的数据来学习采取行动，并通过最佳策略（衡量绩效）提出的建议来支持用户。我们采用一个过程参与者的优化观点，我们建议下一步执行的最佳活动，以响应在复杂的外部环境中发生的事情，而外部因素无法控制。为此，我们研究了一种方法，该方法是通过强化学习从观察到过去执行的最佳政策，并建议开展的最佳活动以优化关键的感兴趣的关键绩效指标。该方法的有效性在从现实生活数据中获取的两种情况下得到了证明。
The rise of process data availability has recently led to the development of data-driven learning approaches. However, most of these approaches restrict the use of the learned model to predict the future of ongoing process executions. The goal of this paper is moving a step forward and leveraging available data to learning to act, by supporting users with recommendations derived from an optimal strategy (measure of performance). We take the optimization perspective of one process actor and we recommend the best activities to execute next, in response to what happens in a complex external environment, where there is no control on exogenous factors. To this aim, we investigate an approach that learns, by means of Reinforcement Learning, the optimal policy from the observation of past executions and recommends the best activities to carry on for optimizing a Key Performance Indicator of interest. The validity of the approach is demonstrated on two scenarios taken from real-life data.
