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Process mining of a multi-agent business simulator


A multi-agent system is a useful modeling architecture in business process modeling in the sense that we can naturally implement participants in a real company with software agents. However, analyzing and interpreting the simulation results of multi-agent models tends to be difficult due to the inherent complexity of the models. In this regard, another discipline—process mining—is useful for such purposes because it has demonstrated its usefulness in analyzing real processes. In this article, our aim is to combine these two disciplines for exploitation in business process modeling and simulation; we extend a multi-agent-based business simulator named Multi-Agent system with Resource-Event-Agent ontology (MAREA) to be able to be analyzed by means of process mining techniques. To this end, we formalize the abstract multi-agent architecture of MAREA and establish its relationship to process mining by defining how execution of a multi-agent system can be recorded as an event log, which is later analyzed by process mining techniques. Based on this definition, we implement functionality to extract event logs from simulation runs in MAREA. For demonstration, we implement a model of a generic trading company in MAREA and perform process structure verification and social network analyzes by means of process mining techniques.

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    Readers may notice that Fig. 1 shows a ‘Sales order’ going to ERP rather than directly to the Accountant. Because ERP is not an agent, it does not receive messages. Therefore, we set the accountant as the receiver of this message. This message serves as the ‘signal’ that the company ERP has modified as a result of the ‘Sales quote acceptance.’

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    This is because ProM associates with each edge the frequency of the cases where the agents are working together.


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This article was supported by the research project ‘Strengthening international cooperation in the area of science, research and education’, which is financed from the budget of the Moravian and Silesian Region (MSK), Czech Republic, Contract No. 01204/2016/RRd.

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Correspondence to Sohei Ito.

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Ito, S., Vymětal, D., Šperka, R. et al. Process mining of a multi-agent business simulator. Comput Math Organ Theory 24, 500–531 (2018).

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  • Business process modeling
  • Business process simulation
  • Multi-agent system
  • Process mining