The paper “An Agent-Based Process Mining Architecture for Emergent Behavior Analysis” has been accepted and presented at the 23rd IEEE EDOC conference in Paris. The paper was part of the Strategic Modeling and Reasoning meets Process Mining (SMPRM) Workshop. The paper is co-authored by Rob Bemthuis, Martijn Koot, Martijn Mes, Faiza Bukhsh, Maria-Eugenia Iacob, and Nirvana Meratnia. The paper can be found here and the presentation here.
AbstractInformation systems leave a traceable digital footprint whenever an action is executed. Business process modelers capture these digital traces to understand the behavior of a system, and to extract actual run-time models of those business processes. Despite the omnipresence of such traces, most organizations face substantial differences between the process specifications and the actual run-time behavior. Analyzing and implementing the results of systems that model business processes tend, however, to be difficult due to the inherent complexity of the models. Moreover, the observed reality in the form of lower-level real-time events, as recorded in event logs, is seldom solely explainable by higher-level process models. In this paper, we propose an architecture to model system-wide behavior by combining process mining with a multi-agent system. Digital traces, in the form of event logs, are used to iteratively mine process models from which agents can learn. The approach is initially applied to a case study of a simplified job-shop factory in which automated guided vehicles (AGVs) carry out transportation tasks. Numerical experiments show that the workflow of a process mining model can be used to enhance the agent-based system, particularly, in analyzing bottlenecks and improving decision-making.