AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909289070723072 |
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| author | Kirchdorfer, Lukas Blümel, Robert Kampik, Timotheus van der Aa, Han Stuckenschmidt, Heiner |
| author_facet | Kirchdorfer, Lukas Blümel, Robert Kampik, Timotheus van der Aa, Han Stuckenschmidt, Heiner |
| contents | Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios. Traditionally, BPS approaches employ a control-flow-first perspective by enriching a process model with simulation parameters. Although such approaches can mimic the behavior of centrally orchestrated processes, such as those supported by workflow systems, current control-flow-first approaches cannot faithfully capture the dynamics of real-world processes that involve distinct resource behavior and decentralized decision-making. Recognizing this issue, this paper introduces AgentSimulator, a resource-first BPS approach that discovers a multi-agent system from an event log, modeling distinct resource behaviors and interaction patterns to simulate the underlying process. Our experiments show that AgentSimulator achieves state-of-the-art simulation accuracy with significantly lower computation times than existing approaches while providing high interpretability and adaptability to different types of process-execution scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08571 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation Kirchdorfer, Lukas Blümel, Robert Kampik, Timotheus van der Aa, Han Stuckenschmidt, Heiner Multiagent Systems Artificial Intelligence Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios. Traditionally, BPS approaches employ a control-flow-first perspective by enriching a process model with simulation parameters. Although such approaches can mimic the behavior of centrally orchestrated processes, such as those supported by workflow systems, current control-flow-first approaches cannot faithfully capture the dynamics of real-world processes that involve distinct resource behavior and decentralized decision-making. Recognizing this issue, this paper introduces AgentSimulator, a resource-first BPS approach that discovers a multi-agent system from an event log, modeling distinct resource behaviors and interaction patterns to simulate the underlying process. Our experiments show that AgentSimulator achieves state-of-the-art simulation accuracy with significantly lower computation times than existing approaches while providing high interpretability and adaptability to different types of process-execution scenarios. |
| title | AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2408.08571 |