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Process discovery studies ways to use event data generated by businessprocesses and recorded by IT systems to construct models that describe theprocesses. Existing discovery algorithms are predominantly concerned withconstructing process models that represent the control flow of the processes.Agent system mining argues that business processes often emerge frominteractions of autonomous agents and uses event data to construct models ofthe agents and their interactions. This paper presents and evaluates AgentMiner, an algorithm for discovering models of agents and their interactionsfrom event data composing the system that has executed the processes whichgenerated the input data. The conducted evaluation using our open-sourceimplementation of Agent Miner and publicly available industrial datasetsconfirms that our algorithm can provide insights into the process participantsand their interaction patterns and often discovers models that describe thebusiness processes more faithfully than process models discovered usingconventional process discovery algorithms.

Agent Miner: An Algorithm for Discovering Agent Systems from Event Data