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

TL;DR

Agent Miner is a novel algorithm that discovers models of autonomous agents and their interactions from event data, providing more accurate insights into business processes than traditional control flow models.

Contribution

The paper introduces Agent Miner, a new algorithm for discovering agent-based models from event data, emphasizing interactions and autonomous behavior in business processes.

Findings

Agent Miner effectively uncovers agent interactions from event data.

The algorithm produces models that better reflect actual business processes.

It outperforms conventional process discovery algorithms in fidelity.

Abstract

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.