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Incorporating Behavioral Recommendations Mined from Event Logs into AI Planning

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Intelligent Information Systems (CAiSE 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 520))

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Abstract

AI planning plays a crucial role in the design and optimization of business processes, providing optimal plans, i.e., sequence of activities, based on manually crafted or formally documented rules. When these plans are executed in business processes, the supporting information systems record a wealth of event data. Analyzing such event data facilitates understanding implicit patterns and recommendations that have the potential to refine planning strategies significantly. In this paper, we introduce a systematic approach to mining these recommendations from event data and integrating them into AI planning, thus creating plans that are informed by both the regulatory hard rules and the flexibility of soft recommendations.

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Acknowledgement

The authors gratefully acknowledge the financial support by the Federal Ministry of Education and Research (BMBF) for the joint project AIStudyBuddy (grant no. 16DHBKI016).

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Authors and Affiliations

  1. Process and Data Science Group, RWTH Aachen University, Aachen, Germany

    Gyunam Park, Majid Rafiei & Wil M. P. van der Aalst

  2. Knowledge-Based Systems Group, RWTH Aachen University, Aachen, Germany

    Hayyan Helal & Gerhard Lakemeyer

Authors
  1. Gyunam Park
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  2. Majid Rafiei
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  3. Hayyan Helal
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  4. Gerhard Lakemeyer
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  5. Wil M. P. van der Aalst
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Corresponding author

Correspondence to Gyunam Park .

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Editors and Affiliations

  1. Anglia Ruskin University, Cambridge, UK

    Shareeful Islam

  2. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Arnon Sturm

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Park, G., Rafiei, M., Helal, H., Lakemeyer, G., van der Aalst, W.M.P. (2024). Incorporating Behavioral Recommendations Mined from Event Logs into AI Planning. In: Islam, S., Sturm, A. (eds) Intelligent Information Systems. CAiSE 2024. Lecture Notes in Business Information Processing, vol 520. Springer, Cham. https://doi.org/10.1007/978-3-031-61000-4_3

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  • DOI: https://doi.org/10.1007/978-3-031-61000-4_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-60999-2

  • Online ISBN: 978-3-031-61000-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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