What Automated Planning Can Do for Business Process Management

  • Andrea MarrellaEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 308)


Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle.


  1. 1.
    van der Aalst, W.M.P.: Business process management: a comprehensive survey. ISRN Softw. Eng. 2013, 37 pages (2013)Google Scholar
  2. 2.
    Lenz, R., Reichert, M.: IT support for healthcare processes - premises, challenges, perspectives. Data Knowl. Eng. 61(1), 39–58 (2007)CrossRefGoogle Scholar
  3. 3.
    Seiger, R., Keller, C., Niebling, F., Schlegel, T.: Modelling complex and flexible processes for smart cyber-physical environments. J. Comput. Sci. 10, 137–148 (2014)CrossRefGoogle Scholar
  4. 4.
    Humayoun, S.R., Catarci, T., de Leoni, M., Marrella, A., Mecella, M., Bortenschlager, M., Steinmann, R.: The WORKPAD user interface and methodology: developing smart and effective mobile applications for emergency operators. In: Stephanidis, C. (ed.) UAHCI 2009. LNCS, vol. 5616, pp. 343–352. Springer, Heidelberg (2009).
  5. 5.
    de Leoni, M., Marrella, A., Russo, A.: Process-aware information systems for emergency management. In: Cezon, M., Wolfsthal, Y. (eds.) ServiceWave 2010. LNCS, vol. 6569, pp. 50–58. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  6. 6.
    Reichert, M., Weber, B.: Enabling Flexibility in Process-Aware Information Systems - Challenges, Methods, Technologies. Springer, Heidelberg (2012)Google Scholar
  7. 7.
    Di Ciccio, C., Marrella, A., Russo, A.: Knowledge-intensive processes: characteristics, requirements and analysis of contemporary approaches. J. Data Semant. 4(1), 29–57 (2015)CrossRefGoogle Scholar
  8. 8.
    Hull, R., Motahari Nezhad, H.R.: Rethinking BPM in a cognitive world: transforming how we learn and perform business processes. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 3–19. Springer, Cham (2016). CrossRefGoogle Scholar
  9. 9.
    Geffner, H., Bonet, B.: A Concise Introduction to Models and Methods for Automated Planning. Morgan & Claypool Publishers, San Rafael (2013)zbMATHGoogle Scholar
  10. 10.
    Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). Google Scholar
  11. 11.
    McDermott, D., et al.: PDDL-the planning domain definition language. Technical report DCS TR-1165, Yale Center for Computational Vision and Control (1998)Google Scholar
  12. 12.
    Geffner, H.: Computational models of planning. Wiley Int. Rev. Cogn. Sci. 4(4) (2013)Google Scholar
  13. 13.
    Geffner, H.: Non-classical planning with a classical planner: the power of transformations. In: Fermé, E., Leite, J. (eds.) JELIA 2014. LNCS, vol. 8761, pp. 33–47. Springer, Cham (2014). Google Scholar
  14. 14.
    La Rosa, M., van der Aalst, W.M., Dumas, M., Milani, F.P.: Business process variability modeling: a survey. ACM Comput. Surv. 50(1) (2013). Article No. 2Google Scholar
  15. 15.
    Schuschel, H., Weske, M.: Triggering replanning in an integrated workflow planning and enactment system. In: Benczúr, A., Demetrovics, J., Gottlob, G. (eds.) ADBIS 2004. LNCS, vol. 3255, pp. 322–335. Springer, Heidelberg (2004). CrossRefGoogle Scholar
  16. 16.
    R-Moreno, M.D., Borrajo, D., Cesta, A., Oddi, A.: Integrating planning and scheduling in workflow domains. Exp. Syst. App. Int. J. 33(2), 389–406 (2007)Google Scholar
  17. 17.
    Ferreira, H., Ferreira, D.: An integrated life cycle for workflow management based on learning and planning. Int. J. Coop. Inf. Syst. 15, 485–505 (2006)CrossRefGoogle Scholar
  18. 18.
    Henneberger, M., Heinrich, B., Lautenbacher, F., Bauer, B.: Semantic-based planning of process models. In: Multikonferenz Wirtschaftsinformatik (2008)Google Scholar
  19. 19.
    Marrella, A., Lespérance, Y.: Synthesizing a library of process templates through partial-order planning algorithms. In: Nurcan, S., Proper, H.A., Soffer, P., Krogstie, J., Schmidt, R., Halpin, T., Bider, I. (eds.) BPMDS/EMMSAD -2013. LNBIP, vol. 147, pp. 277–291. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  20. 20.
    Marrella, A., Lesperance, Y.: A planning approach to the automated synthesis of template-based process models. Serv. Oriented Comput. Appl. 11, 367–392 (2013)CrossRefGoogle Scholar
  21. 21.
    Jarvis, P., et al.: Exploiting AI technologies to realise adaptive workflow systems. In: Proceedings of the AAAI Workshop on Agent-Based Systems in the Business Context (1999)Google Scholar
  22. 22.
    Gajewski, M., Meyer, H., Momotko, M., Schuschel, H., Weske, M.: Dynamic failure recovery of generated workflows. In: DEXA 2005. IEEE Computer Society Press (2005)Google Scholar
  23. 23.
    Marrella, A., Mecella, M., Sardina, S.: SmartPM: an adaptive process management system through situation calculus, IndiGolog, and classical planning. In: KR. AAAI Press (2014)Google Scholar
  24. 24.
    Marrella, A., Mecella, M., Sardina, S.: Intelligent process adaptation in the SmartPM system. ACM Trans. Intell. Syst. Technol. 8(2) (2016). Article No. 25Google Scholar
  25. 25.
    Marrella, A., Mecella, M.: Continuous planning for solving business process adaptivity. In: Halpin, T., Nurcan, S., Krogstie, J., Soffer, P., Proper, E., Schmidt, R., Bider, I. (eds.) BPMDS/EMMSAD -2011. LNBIP, vol. 81, pp. 118–132. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  26. 26.
    Marrella, A., Mecella, M., Russo, A.: Featuring automatic adaptivity through workflow enactment and planning. In: CollaborateCom 2011. IEEE (2011)Google Scholar
  27. 27.
    Marrella, A., Russo, A., Mecella, M.: Planlets: automatically recovering dynamic processes in YAWL. In: Meersman, R., Panetto, H., Dillon, T., Rinderle-Ma, S., Dadam, P., Zhou, X., Pearson, S., Ferscha, A., Bergamaschi, S., Cruz, I.F. (eds.) OTM 2012. LNCS, vol. 7565, pp. 268–286. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  28. 28.
    Bucchiarone, A., Pistore, M., Raik, H., Kazhamiakin, R.: Adaptation of service-based business processes by context-aware replanning. In: SOCA 2011. IEEE (2011)Google Scholar
  29. 29.
    van Beest, N.R., Kaldeli, E., Bulanov, P., Wortmann, J.C., Lazovik, A.: Automated runtime repair of business processes. Inf. Syst. 39, 45–79 (2014)CrossRefGoogle Scholar
  30. 30.
    van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer, Heidelberg (2016)CrossRefGoogle Scholar
  31. 31.
    Adriansyah, A., van Dongen, B.F., Zannone, N.: Controlling break-the-glass through alignment. In: SOCIALCOM 2013. IEEE Computer Society (2013)Google Scholar
  32. 32.
    de Leoni, M., Maggi, F.M., van der Aalst, W.M.P.: Aligning event logs and declarative process models for conformance checking. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 82–97. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  33. 33.
    de Leoni, M., Marrella, A.: Aligning real process executions and prescriptive process models through automated planning. Expert Syst. Appl. 82, 162–183 (2017)CrossRefGoogle Scholar
  34. 34.
    Di Francescomarino, C., Ghidini, C., Tessaris, S., Sandoval, I.V.: Completing workflow traces using action languages. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 314–330. Springer, Cham (2015). CrossRefGoogle Scholar
  35. 35.
    De Giacomo, G., Maggi, F.M., Marrella, A., Patrizi, F.: On the disruptive effectiveness of automated planning for LTLf-based trace alignment. In: AAAI 2017. AAAI Press (2017)Google Scholar
  36. 36.
    Pistore, M., Traverso, P., Bertoli, P., Marconi, A.: Automated synthesis of composite BPEL4WS web services. In: ICWS 2005. IEEE Computer Society (2005)Google Scholar
  37. 37.
    Pistore, M., Traverso, P., Bertoli, P., Marconi, A.: Automated synthesis of executable web service compositions from BPEL4WS processes. In: WWW 2005. ACM (2005)Google Scholar
  38. 38.
    Georgievski, I., Aiello, M.: Automated planning for ubiquitous computing. ACM Comput. Surv. 49(4), 1–46 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Sapienza - University of RomeRomeItaly

Personalised recommendations