Process discovery studies ways to use event data generated by business processes and recorded by IT systems to construct models that describe the processes. Existing discovery algorithms are predominantly concerned with constructing process models that represent the control flow of the processes. Agent system mining argues that business processes often emerge from interactions of autonomous agents and uses event data to construct models of the agents and their interactions. This paper presents and evaluates Agent Miner, an algorithm for discovering models of agents and their interactions from event data composing the system that has executed the processes which generated the input data. The conducted evaluation using our open-source implementation of Agent Miner and publicly available industrial datasets confirms that our algorithm can provide insights into the process participants and their interaction patterns and often discovers models that describe the business processes more faithfully than process models discovered using conventional process discovery algorithms.

翻译：流程发现研究如何利用业务流程产生的、由IT系统记录的事件数据，构建描述这些流程的模型。现有发现算法主要关注构建表示流程控制流的流程模型。智能体系统挖掘认为，业务流程通常源于自主智能体之间的交互，并利用事件数据构建智能体及其交互的模型。本文提出并评估了Agent Miner算法，该算法能从执行了生成输入数据的流程的系统所组成的事件数据中，发现智能体及其交互的模型。通过使用我们开源的Agent Miner实现以及公开可用的工业数据集进行评估，结果证实：该算法能揭示流程参与者及其交互模式，并且通常能发现比传统流程发现算法更忠实地描述业务流程的模型。